A population-based study of the effect of pregnancy history on risk of stillbirth

Risk of recurrent stillbirth and neonatal mortality: mother-specific random effects analysis using longitudinal panel data from Indonesia (2000 - 2014)

Background

Despite significant government investments to improve birth outcomes in low and middle-income countries over the past several decades, stillbirth and neonatal mortality continue to be persistent public health problems. While they are different outcomes, there is little evidence regarding their shared and unique population-level risk factors over a mother’s reproductive lifespan. Data gaps and measurement challenges have left several areas in this field unexplored, especially assessing the risk of stillbirth or neonatal mortality over successive pregnancies to the same woman. This study aimed to assess the risk of stillbirth and neonatal mortality in Indonesia during 2000–2014, using maternal birth histories from the Indonesia Family Life Survey panel data.

Methods

Data from three panels were combined to create right-censored birth histories. There were 5,002 unique multiparous mothers with at least two singleton births in the sample. They reported 12,761 total births and 12,507 live births. Random effects (RE) models, which address the dependency of variance in births to the same mother, were fitted assuming births to the same mother shared unobserved risk factors unique to the mother.

Results

The main finding was that there having had a stillbirth increased the odds of another stillbirth nearly seven-fold and that of subsequent neonatal mortality by over two-fold. Having had a neonatal death was not associated with a future neonatal death. Mothers who were not educated and nullipara were much more likely to experience a neonatal death while mothers who had a prior neonatal death had no risk of another neonatal death due to unmeasured factors unique to the mother.

Conclusions

The results suggest that for stillbirths, maternal heterogeneity, as explained by a prior stillbirth, could capture underlying pathology while the relationship between observed risk factors and neonatal mortality could be much more dependent on context. Establishing previous adverse outcomes such as neonatal deaths and stillbirth could help identify high-risk pregnancies during prenatal care, inform interventions, and improve health policy.

Assessment and validation of the Community Maternal Danger Score algorithm

Background

High rates of maternal mortality in low-and-middle-income countries (LMICs) are associated with the lack of skilled birth attendants (SBAs) at delivery. Risk analysis tools may be useful to identify pregnant women who are at risk of mortality in LMICs. We sought to develop and validate a low-cost maternal risk tool, the Community Maternal Danger Score (CMDS), which is designed to identify pregnant women who need an SBA at delivery.

Methods

To design the CMDS algorithm, an initial scoping review was conducted to identify predictors of the need for an SBA. Medical records of women who delivered at the Federal Medical Centre in Makurdi, Nigeria (2019–2020) were examined for predictors identified from the literature review. Outcomes associated with the need for an SBA were recorded: caesarean section, postpartum hemorrhage, eclampsia, and sepsis. A maternal mortality ratio (MMR) was determined. Multivariate logistic regression analysis and area under the curve (AUC) were used to assess the predictive ability of the CMDS algorithm.

Results

Seven factors from the literature predicted the need for an SBA: age (under 20 years of age or 35 and older), parity (nulliparity or grand-multiparity), BMI (underweight or overweight), fundal height (less than 35 cm or 40 cm and over), adverse obstetrical history, signs of pre-eclampsia, and co-existing medical conditions. These factors were recorded in 589 women of whom 67% required an SBA (n = 396) and 1% died (n = 7). The MMR was 1189 per 100,000 (95% CI 478–2449). Signs of pre-eclampsia, obstetrical history, and co-existing conditions were associated with the need for an SBA. Age was found to interact with parity, suggesting that the CMDS requires adjustment to indicate higher risk among younger multigravida and older primigravida women. The CMDS algorithm had an AUC of 0.73 (95% CI 0.69–0.77) for predicting whether women required an SBA, and an AUC of 0.85 (95% CI 0.67–1.00) for in-hospital mortality.

Conclusions

The CMDS is a low-cost evidence-based tool that uses 7 risk factors assessed on 589 women from Makurdi. Non-specialist health workers can use the CMDS to standardize assessment and encourage pregnant women to seek an SBA in preparation for delivery, thus improving care in countries with high rates of maternal mortality.

Analysis of the causes and influencing factors of fetal loss in advanced maternal age: a nested case-control study

BACKGROUND

The risk of fetal loss is higher among ≥35-year-olds than younger women. The present study aimed to explore the causes and factors influencing fetal loss in advanced maternal age (AMA).

METHODS

AMA women with singleton fetuses (< 14 gestational weeks) who underwent their first prenatal examination in the Obstetrics Department of Fujian Maternity and Child Health Hospital from December 2018 to June 2020 were included in this cohort study. Those who terminated the pregnancy before 14 gestational weeks were excluded. A baseline survey was conducted, and follow-up was carried out until the termination of the pregnancy. Clinical data were extracted to analyse the causes of fetal loss among them. In the nested case-control study, the AMA women with fetal loss were enrolled as the case group, and women without fetal loss in the same period were enrolled as the control group, in a 1:2 ratio matched by age and gestational weeks. Logistic regression models were used to analyse the factors influencing fetal loss.

RESULTS

A total of 239 women with fetal loss and 478 controls were enrolled. The causes of fetal loss were most often fetal factors, followed by maternal factors, umbilical cord factors, and placental factors. Multivariate logistic regression analysis indicated that junior high school education and below (adjusted odds ratio (aOR) = 5.13, 95% confidence interval (CI): 2.19-12.02), senior high school education (aOR = 4.91, 95% CI: 2.09-11.54), residence in a rural area (aOR = 2.85, 95% CI: 1.92-4.25), unemployment (aOR = 1.81, 95% CI: 1.20-2.71), spontaneous abortion history (aOR = 1.88, 95% CI: 1.26-2.80), preterm birth history (aOR = 11.08, 95% CI: 2.90-42.26), hypertensive disorders of pregnancy (aOR = 7.20, 95% CI: 2.24-23.12), and preterm premature rupture of membranes (aOR = 4.12, 95% CI: 1.53-11.11) were risk factors for fetal loss.

CONCLUSIONS

Low educational level, unemployment, abnormal pregnancy/labor history, and pregnancy complications were correlated with the incidence of fetal loss in AMA. Thus, early identification as well as a targeted intervention, should be conducted.

Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study

Background

Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the features.

Method

A two-step stack ensemble classifier is proposed for classifying the instances into stillbirth and livebirth at the first step and then, classifying stillbirth before delivery from stillbirth during the labor at the second step. The proposed SE has two consecutive layers including the same classifiers. The base classifiers in each layer are decision tree, Gradient boosting classifier, logistics regression, random forest and support vector machines which are trained independently and aggregated based on Vote boosting method. Moreover, a new feature ranking method is proposed in this study based on mean decrease accuracy, Gini Index and model coefficients to find high-ranked features.

Results

IMAN registry dataset is used in this study considering all births at or beyond 28th gestational week from 2016/04/01 to 2017/01/01 including 1,415,623 live birth and 5502 stillbirth cases. A combination of maternal demographic features, clinical history, fetal properties, delivery descriptors, environmental features, healthcare service provider descriptors and socio-demographic features are considered. The experimental results show that our proposed SE outperforms the compared classifiers with the average accuracy of 90%, sensitivity of 91%, specificity of 88%. The discrimination of the proposed SE is assessed and the average AUC of ±95%, CI of 90.51% ±1.08 and 90% ±1.12 is obtained on training dataset for model development and test dataset for external validation, respectively. The proposed SE is calibrated using isotopic nonparametric calibration method with the score of 0.07. The process is repeated 10,000 times and AUC of SE classifiers using random different training datasets as null distribution. The obtained p-value to assess the specificity of the proposed SE is 0.0126 which shows the significance of the proposed SE.

Conclusions

Gestational age and fetal height are two most important features for discriminating livebirth from stillbirth. Moreover, hospital, province, delivery main cause, perinatal abnormality, miscarriage number and maternal age are the most important features for classifying stillbirth before and during delivery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-021-03658-z.

A survey of influencing factors of missed abortion during the two-child peak period

This study aimed to investigate the influencing factors of missed abortion during the two-child peak period in China. 220 pregnant women were divided into observation (presence of missed abortion, 100 cases) and control group (no presence of missed abortion, 120 cases). The single factor analysis of clinical data showed that, advanced age, premarital examination, genitalia abnormality, luteal insufficiency, spouse semen abnormality, mycoplasma infection, chlamydia infection, sexually transmitted diseases, perm or dyeing hair in pregnancy, radiation overload, primipara, spontaneous abortion history, smoking, drinking and overly intimate with pets had significant difference between observation and control group (p < .05). The logistic regression analysis results showed that, the advanced age, genital abnormality, luteal insufficiency, spouse sperm abnormality, pregnancy infection, primipara, spontaneous abortion history and bad life habits were the main risk factors of missed abortion. In the intervention for prevention of missed abortion, these factors should be paid more attention.Impact statementWhat is already known on this subject? There are many complex factors affecting the embryonic development and causing the missed abortion.What do the results of this study add? The advanced age, genital abnormality, luteal insufficiency, spouse sperm abnormality, pregnancy infection, primipara, spontaneous abortion history and bad life habits are the main risk factors of missed abortion.What are the implications of these findings for clinical practice and/or further research? These findings can provide a theoretical basis for the further prevention of missed abortion.