Recurrence of perinatal death in Northern Tanzania: a registry based cohort study

Racial and ethnic differences in the risk of recurrent preterm or small for gestational age births in the United States: a systematic review and stratified analysis

BACKGROUND

The risk of recurrent adverse birth outcomes has been reported worldwide, but there are limited estimates of these risks by social subgroups such as race and ethnicity in the United States. We assessed racial and ethnic disparities in the risk of recurrent adverse birth outcomes, including preterm birth, low birthweight, fetal growth restriction, small for gestational age, stillbirth, and neonatal mortality in the U.S.

METHODS

We searched MEDLINE, CINAHL Complete, Web of Science, and Scopus from the date of inception to April 5, 2022. We identified 3,540 articles for a title and abstract review, of which 80 were selected for full-text review. Studies were included if they focused on the recurrence of any of the six outcomes listed in the objectives. Study quality was assessed using the NIH Study Quality Assessment Tool. Heterogeneity across studies was too large for meta-analysis, but race and ethnicity-stratified estimates and tests for homogeneity results were reported.

RESULTS

Six studies on recurrent preterm birth and small for gestational age were included. Pooled comparisons showed a higher risk of recurrent preterm birth and small for gestational age for all women. Stratified race comparisons showed a higher but heterogeneous risk of recurrence of preterm birth across Black and White women. Relative risks of recurrent preterm birth ranged from 2.02 [1.94, 2.11] to 2.86 [2.40, 3.39] for Black women and from 3.23 [3.07, 3.39] to 3.92 [3.35, 4.59] for White women. The evidence was weak for race and ethnicity stratification for Hispanic and Asian women for both outcomes.

CONCLUSIONS

Disparities exist in the recurrence of preterm birth, and race/ethnicity-concordant comparisons suggest race is an effect modifier for recurrent preterm birth for Black and White women. Due to the small number of studies, no conclusions could be made for small for gestational age or Hispanic and Asian groups. The results pose new research areas to better understand race-based differences in recurrent adverse birth outcomes.

The effect of low-molecular-weight heparin on live birth rate of patients with unexplained early recurrent pregnancy loss: A two-arm randomized clinical trial

BACKGROUND

The effect of anticoagulant medication in unexplained early recurrent pregnancy loss (RPL) patients is controversial. This clinical trial evaluated the effect of low-molecular-weight heparin (LMWH) on pregnancy outcomes in these patients.

MATERIALS AND METHODS

The study was performed as a single-blind randomized clinical trial between 2016 and 2018. Samples were selected from patients who were referred to Avicenna RPL clinic with a history of at least two previously happened early unexplained miscarriages. The eligibility was defined strictly to select unexplained RPL patients homogenously. One hundred and seventy-three patients who got pregnant recently were allocated randomly into two groups LMWH plus low-dose aspirin treatment (Group A = 85) and low-dose aspirin treatment only (Group B = 88)) and were followed up till their pregnancy termination (delivery/abortion). A per-protocol analysis was carried out and all statistical tests were two-sided with a P < 0.05 significance level.

RESULTS

The live birth rates (LBRs) in Groups A and B were 78% and 77.1%, respectively, which did not show any statistically significant difference between the two groups, neither in rates nor in time of abortion. In subgroup analysis for polycystic ovary syndrome (PCOS) patients, the odds ratio for study outcome (intervention/control) was 2.25 (95% confidence interval: 0.65-7.73). There was no major adverse event whereas minor bleeding was observed in 18% of patients in Group A.

CONCLUSION

LMWH does not improve the LBR in unexplained RPL patients, however, it is recommended to evaluate its effect separately in PCOS patients.

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.

Validating machine learning models for the prediction of labour induction intervention using routine data: a registry-based retrospective cohort study at a tertiary hospital in northern Tanzania

OBJECTIVES

We aimed at identifying the important variables for labour induction intervention and assessing the predictive performance of machine learning algorithms.

SETTING

We analysed the birth registry data from a referral hospital in northern Tanzania. Since July 2000, every birth at this facility has been recorded in a specific database.

PARTICIPANTS

21 578 deliveries between 2000 and 2015 were included. Deliveries that lacked information regarding the labour induction status were excluded.

PRIMARY OUTCOME

Deliveries involving labour induction intervention.

RESULTS

Parity, maternal age, body mass index, gestational age and birth weight were all found to be important predictors of labour induction. Boosting method demonstrated the best discriminative performance (area under curve, AUC=0.75: 95% CI (0.73 to 0.76)) while logistic regression presented the least (AUC=0.71: 95% CI (0.70 to 0.73)). Random forest and boosting algorithms showed the highest net-benefits as per the decision curve analysis.

CONCLUSION

All of the machine learning algorithms performed well in predicting the likelihood of labour induction intervention. Further optimisation of these classifiers through hyperparameter tuning may result in an improved performance. Extensive research into the performance of other classifier algorithms is warranted.

Predictors of singleton preterm birth using multinomial regression models accounting for missing data: A birth registry-based cohort study in northern Tanzania

BACKGROUND

Preterm birth is a significant contributor of under-five and newborn deaths globally. Recent estimates indicated that, Tanzania ranks the tenth country with the highest preterm birth rates in the world, and shares 2.2% of the global proportion of all preterm births. Previous studies applied binary regression models to determine predictors of preterm birth by collapsing gestational age at birth to <37 weeks. For targeted interventions, this study aimed to determine predictors of preterm birth using multinomial regression models accounting for missing data.

METHODS

We carried out a secondary analysis of cohort data from the KCMC zonal referral hospital Medical Birth Registry for 44,117 women who gave birth to singletons between 2000-2015. KCMC is located in the Moshi Municipality, Kilimanjaro region, northern Tanzania. Data analysis was performed using Stata version 15.1. Assuming a nonmonotone pattern of missingness, data were imputed using a fully conditional specification (FCS) technique under the missing at random (MAR) assumption. Multinomial regression models with robust standard errors were used to determine predictors of moderately to late ([32,37) weeks of gestation) and very/extreme (<32 weeks of gestation) preterm birth.

RESULTS

The overall proportion of preterm births among singleton births was 11.7%. The trends of preterm birth were significantly rising between the years 2000-2015 by 22.2% (95%CI 12.2%, 32.1%, p<0.001) for moderately to late preterm and 4.6% (95%CI 2.2%, 7.0%, p = 0.001) for very/extremely preterm birth category. After imputation of missing values, higher odds of moderately to late preterm delivery were among adolescent mothers (OR = 1.23, 95%CI 1.09, 1.39), with primary education level (OR = 1.28, 95%CI 1.18, 1.39), referred for delivery (OR = 1.19, 95%CI 1.09, 1.29), with pre-eclampsia/eclampsia (OR = 1.77, 95%CI 1.54, 2.02), inadequate (<4) antenatal care (ANC) visits (OR = 2.55, 95%CI 2.37, 2.74), PROM (OR = 1.80, 95%CI 1.50, 2.17), abruption placenta (OR = 2.05, 95%CI 1.32, 3.18), placenta previa (OR = 4.35, 95%CI 2.58, 7.33), delivery through CS (OR = 1.16, 95%CI 1.08, 1.25), delivered LBW baby (OR = 8.08, 95%CI 7.46, 8.76), experienced perinatal death (OR = 2.09, 95%CI 1.83, 2.40), and delivered male children (OR = 1.11, 95%CI 1.04, 1.20). Maternal age, education level, abruption placenta, and CS delivery showed no statistically significant association with very/extremely preterm birth. The effect of (<4) ANC visits, placenta previa, LBW, and perinatal death were more pronounced on the very/extremely preterm compared to the moderately to late preterm birth. Notably, extremely higher odds of very/extreme preterm birth were among the LBW babies (OR = 38.34, 95%CI 31.87, 46.11).

CONCLUSIONS

The trends of preterm birth have increased over time in northern Tanzania. Policy decisions should intensify efforts to improve maternal and child care throughout the course of pregnancy and childbirth towards preterm birth prevention. For a positive pregnancy outcome, interventions to increase uptake and quality of ANC services should also be strengthened in Tanzania at all levels of care, where several interventions can easily be delivered to pregnant women, especially those at high-risk of experiencing adverse pregnancy outcomes.

Joint Modeling of Singleton Preterm Birth and Perinatal Death Using Birth Registry Cohort Data in Northern Tanzania

Understanding independent and joint predictors of adverse pregnancy outcomes is essential to inform interventions toward achieving sustainable development goals. We aimed to determine the joint predictors of preterm birth and perinatal death among singleton births in northern Tanzania based on cohort data from the Kilimanjaro Christian Medical Center (KCMC) zonal referral hospital birth registry between 2000 and 2017. We determined the joint predictors of preterm birth and perinatal death using the random-effects models to account for the correlation between these outcomes. The joint predictors of higher preterm birth and perinatal death risk were inadequate (<4) antenatal care (ANC) visits, referred for delivery, experiencing pre-eclampsia/eclampsia, postpartum hemorrhage, low birth weight, abruption placenta, and breech presentation. Younger maternal age (15-24 years), premature rupture of membranes, placenta previa, and male children had higher odds of preterm birth but a lessened likelihood of perinatal death. These findings suggest ANC is a critical entry point for delivering the recommended interventions to pregnant women, especially those at high risk of experiencing adverse pregnancy outcomes. Improved management of complications during pregnancy and childbirth and the postnatal period may eventually lead to a substantial reduction of adverse perinatal outcomes and improving maternal and child health.

Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania

OBJECTIVE

We aimed to determine the key predictors of perinatal deaths using machine learning models compared with the logistic regression model.

DESIGN

A secondary data analysis using the Kilimanjaro Christian Medical Centre (KCMC) Medical Birth Registry cohort from 2000 to 2015. We assessed the discriminative ability of models using the area under the receiver operating characteristics curve (AUC) and the net benefit using decision curve analysis.

SETTING

The KCMC is a zonal referral hospital located in Moshi Municipality, Kilimanjaro region, Northern Tanzania. The Medical Birth Registry is within the hospital grounds at the Reproductive and Child Health Centre.

PARTICIPANTS

Singleton deliveries (n=42 319) with complete records from 2000 to 2015.

PRIMARY OUTCOME MEASURES

Perinatal death (composite of stillbirths and early neonatal deaths). These outcomes were only captured before mothers were discharged from the hospital.

RESULTS

The proportion of perinatal deaths was 3.7%. There were no statistically significant differences in the predictive performance of four machine learning models except for bagging, which had a significantly lower performance (AUC 0.76, 95% CI 0.74 to 0.79, p=0.006) compared with the logistic regression model (AUC 0.78, 95% CI 0.76 to 0.81). However, in the decision curve analysis, the machine learning models had a higher net benefit (ie, the correct classification of perinatal deaths considering a trade-off between false-negatives and false-positives)-over the logistic regression model across a range of threshold probability values.

CONCLUSIONS

In this cohort, there was no significant difference in the prediction of perinatal deaths between machine learning and logistic regression models, except for bagging. The machine learning models had a higher net benefit, as its predictive ability of perinatal death was considerably superior over the logistic regression model. The machine learning models, as demonstrated by our study, can be used to improve the prediction of perinatal deaths and triage for women at risk.

Predictors of perinatal death in the presence of missing data: A birth registry-based study in northern Tanzania

Background

More than five million perinatal deaths occur each year globally. Despite efforts put forward during the millennium development goals era, perinatal deaths continue to increase relative to under-five deaths, especially in low- and middle-income countries. This study aimed to determine predictors of perinatal death in the presence of missing data using birth registry data from Kilimanjaro Christian Medical Center (KCMC), between 2000–2015.

Methods

This was a retrospective cohort study from the medical birth registry at KCMC referral hospital located in Moshi Municipality, Kilimanjaro region, northern Tanzania. Data were analyzed using Stata version 15.1. Multiple imputation by fully conditional specification (FCS) was used to impute missing values. Generalized estimating equations (GEE) were used to determine the marginal effects of covariates on perinatal death using a log link mean model with robust standard errors. An exchangeable correlation structure was used to account for the dependence of observations within mothers.

Results

Among 50,487 deliveries recorded in the KCMC medical birth registry between 2000–2015, 4.2% (95%CI 4.0%, 4.3%) ended in perinatal death (equivalent to a perinatal mortality rate (PMR) of 41.6 (95%CI 39.9, 43.3) deaths per 1,000 births). After the imputation of missing values, the proportion of perinatal death remained relatively the same. The risk of perinatal death was significantly higher among deliveries from mothers who resided in rural compared to urban areas (RR = 1.241, 95%CI 1.137, 1.355), with primary education level (RR = 1.201, 95%CI 1.083, 1.332) compared to higher education level, with <4 compared to ≥4 antenatal care (ANC) visits (RR = 1.250, 95%CI 1.146, 1.365), with postpartum hemorrhage (PPH) (RR = 2.638, 95%CI 1.997, 3.486), abruption placenta (RR = 4.218, 95%CI 3.438, 5.175), delivered a low birth weight baby (LBW) (RR = 4.210, 95%CI 3.788, 4.679), male child (RR = 1.090, 95%CI 1.007, 1.181), and were referred for delivery (RR = 2.108, 95%CI 1.919, 2.317). On the other hand, deliveries from mothers who experienced premature rupture of the membranes (PROM) (RR = 0.411, 95%CI 0.283, 0.598) and delivered through cesarean section (CS) (RR = 0.662, 95%CI 0.604, 0.724) had a lower risk of perinatal death.

Conclusions

Perinatal mortality in this cohort is higher than the national estimate. Higher risk of perinatal death was associated with low maternal education level, rural residence, <4 ANC visits, PPH, abruption placenta, LBW delivery, child’s sex, and being referred for delivery. Ignoring missing values in the analysis of adverse pregnancy outcomes produces biased covariate coefficients and standard errors. Close clinical follow-up of women at high risk of experiencing perinatal death, particularly during ANC visits and delivery, is of high importance to increase perinatal survival.

Prevalence and risk factors for caesarean delivery following labor induction at a tertiary hospital in North Tanzania: a retrospective cohort study (2000-2015)

BACKGROUND

Labor induction is among the common and widely practiced obstetric interventions aiming at achieving vaginal delivery. However, cesarean section (CS) delivery incidences have been reported following its use. This study aimed at determining the prevalence and risk factors for caesarean delivery following labor induction among women who gave birth at a tertiary hospital in north-Tanzania.

METHODS

A hospital-based retrospective cohort study was designed using maternally-linked data from Kilimanjaro Christian Medical Centre (KCMC) birth registry among women who gave birth to singleton babies from the year 2000 to 2015. All induced deliveries done in this period were studied. Women with multiple pregnancy, missing information on delivery mode and those with history of CS delivery were excluded. Relative risk and 95% Confidence Interval for risk factors for CS delivery following labor induction were estimated using log-binomial regression models. Robust variance estimation was used to account for repeated deliveries from the same subject.

RESULTS

A total of 1088 deliveries were analysed. The prevalence of CS following labour induction was 26.75%. Independent risk factors for CS delivery were; primiparity (RR = 1.46; 95% CI: 1.18-1.81), high birthweight (RR =1.28; 95% CI: 1.02-1.61), post-term pregnancy (RR = 1.45; 95% CI: 1.09-1.93), and urban residence (RR =1.29; 95%CI: 1.05-1.58).

CONCLUSION

In patients undergoing labor induction, primiparity, high birthweight, post dates and urban residence were found to associate with an elevated risk of caesarean delivery. Assessment of these factors prior to labor induction intervention is warranted to reduce adverse pregnancy outcomes associated with emergency caesarean delivery.

The mnemonic code of pregnancy: Comparative analyses of pregnancy success and complication risk in first and second human pregnancies

Obstetrical complications such as spontaneous abortion/miscarriage, fetal growth restriction, preeclampsia or preterm birth occur in approx. 15% of human pregnancies. Clinical experts often state that a previous uncomplicated pregnancy reduces the risk for complications in subsequent pregnancies. Vice versa, a prior pregnancy affected by obstetrical complications increases the risk for reoccurrence. However, published evidence directly underpinning these clinical statements is sparse. Considering that the maternal immune adaptation may be causally involved in determining the outcome of subsequent pregnancies, a comprehensive analysis of clinical data was long overdue. We here present a systematic analysis of clinical data using a PubMed-based approach to identify human studies with relevant information on birth weight and incidences of pregnancy complications in first and second pregnancies. From initially 18,592 publications, 37 studies were included in the quantitative data analysis. Women with a previous pregnancy affected by complications where a derailed immune response can be inferred have a 2.2-3.2-fold increased risk to be affected again in a subsequent pregnancy. Conversely, a normally progressing primary pregnancy reduced the risk for complications in a subsequent pregnancy by 35-65%. Moreover, an uncomplicated primary pregnancy was associated with a 4.2% increased birth weight in a following pregnancy without a difference in gestational age at delivery. In conclusion, the increased birth weight after previously uncomplicated pregnancies suggests that an immune memory is mounted during primary pregnancies. This immune memory may promote the successful outcome of subsequent pregnancies or - if missing or compromised - account for a risk perpetuation of pregnancy complications.

A structural equation analysis on the relationship between maternal health services utilization and newborn health outcomes: a cross-sectional study in Eastern Uganda

Background

Neonatal and maternal health services have a bearing on neonatal mortality. Direct and indirect factors affecting neonatal health outcomes therefore require understanding to enable well-targeted interventions. This study, therefore, assessed the interrelationship between newborn health outcomes and maternal service utilization factors.

Methods

We investigated maternal health utilization factors using health facility delivery and at least four Antenatal Care (ANC) visits; and newborn health outcomes using newborn death and low birth weight (LBW). We used data from a household cross-sectional survey that was conducted in 2015 in Kamuli, Pallisa and Kibuku districts. We interviewed 1946 women who had delivered in the last 12 months. The four interrelated (Endogenous) outcomes were ANC attendance, health facility delivery, newborn death, and LBW. We performed analysis using a structural equation modeling technique.

Results

A history of newborn death (aOR = 12.64, 95% CI 5.31–30.10) and birth of a LBW baby (aOR = 3.51, 95% CI 1.48–8.37) were directly related to increased odds of newborn death. Factors that reduced the odds of LBW as a mediating factor for newborn death were ANC fourth time attendance (aOR = 0.62, 95% CI 0.45–0.85), having post-primary level education (aOR = 0.68, 95% CI 0.46–0.98) compared to none and being gravida three (aOR = 0.49, 95% CI 0.26–0.94) compared to being gravida one. Mother’s age group, 20–24 (aOR = 0.24, 95% CI 0.08–0.75) and 25–29 years (aOR = 0.20, 95% CI 0.05–0.86) compared to 15–19 years was also associated with reduced odds of LBW. Additionally, ANC visits during the first trimester (aOR = 2.04, 95% CI 1.79–2.34), and village health teams (VHTs) visits while pregnant (aOR = 1.14, 95% CI 1.01–1.30) were associated with increased odds of at least four ANC visits, which is a mediating factor for health facility delivery, LBW and newborn death. Surprisingly, newborn death was not significantly different between health facility and community deliveries.

Conclusions

Attending ANC at least four times was a mediating factor for reduced newborn death and low birth weight. Interventions in maternal health and newborn health should focus on factors that increase ANC fourth time attendance and those that reduce LBW especially in resource-limited settings. Targeting women with high-risk pregnancies is also crucial for reducing newborn deaths.

Electronic supplementary material

The online version of this article (doi:10.1186/s12884-017-1289-5) contains supplementary material, which is available to authorized users.

Effect of interpregnancy interval on adverse pregnancy outcomes in northern Tanzania: a registry-based retrospective cohort study

Background

Both short and long interpregnancy intervals have been associated with an increased risk of adverse pregnancy outcomes. There is limited information about the impact of interpregnancy interval on pregnancy (IPI) outcomes in Tanzania. The objective of this study was to assess the effect of IPI on adverse pregnancy outcomes.

Methods

We performed a retrospective cohort study using maternally-linked data from Kilimanjaro Christian Medical Centre (KCMC) birth registry. A total of 17,030 singlet births from women who delivered singleton infant at KCMC from 2000 to 2010 were studied. Women with multi-fetal gestations and those who were referred from rural areas for various medical reasons were excluded. Outcome variables were preterm birth, low birth weight infants and perinatal death. A multiple logistic regression was used to assess the association between IPI and pregnancy outcomes.

Results

The median IPI was 36 months. Compared with IPIs of 24–36 months (referent group), short interpregnancy intervals (<24 months) was associated with preterm delivery (OR 1 · 52; 95 % CI 1.31–1.74); low birth weight (OR 1 · 61; 95 % CI 1 · 34–1.72) and perinatal death, (OR 1 · 63; 95 % CI 1.22–1.91). The IPI of 37–59 months or longer were also associated with higher risks of preterm birth and low birth weight, but not with perinatal death.

Conclusions

Our study confirmed that both short and long IPI are independent risk factors for adverse pregnancy outcomes. These finding emphasize the importance of providing support for family planning programs which will support optimal IPI and improve pregnancy outcomes.

eRegistries: Electronic registries for maternal and child health

BACKGROUND

The Global Roadmap for Health Measurement and Accountability sees integrated systems for health information as key to obtaining seamless, sustainable, and secure information exchanges at all levels of health systems. The Global Strategy for Women's, Children's and Adolescent's Health aims to achieve a continuum of quality of care with effective coverage of interventions. The WHO and World Bank recommend that countries focus on intervention coverage to monitor programs and progress for universal health coverage. Electronic health registries - eRegistries - represent integrated systems that secure a triple return on investments: First, effective single data collection for health workers to seamlessly follow individuals along the continuum of care and across disconnected cadres of care providers. Second, real-time public health surveillance and monitoring of intervention coverage, and third, feedback of information to individuals, care providers and the public for transparent accountability. This series on eRegistries presents frameworks and tools to facilitate the development and secure operation of eRegistries for maternal and child health.

METHODS

In this first paper of the eRegistries Series we have used WHO frameworks and taxonomy to map how eRegistries can support commonly used electronic and mobile applications to alleviate health systems constraints in maternal and child health. A web-based survey of public health officials in 64 low- and middle-income countries, and a systematic search of literature from 2005-2015, aimed to assess country capacities by the current status, quality and use of data in reproductive health registries.

RESULTS

eRegistries can offer support for the 12 most commonly used electronic and mobile applications for health. Countries are implementing health registries in various forms, the majority in transition from paper-based data collection to electronic systems, but very few have eRegistries that can act as an integrating backbone for health information. More mature country capacity reflected by published health registry based research is emerging in settings reaching regional or national scale, increasingly with electronic solutions. 66 scientific publications were identified based on 32 registry systems in 23 countries over a period of 10 years; this reflects a challenging experience and capacity gap for delivering sustainable high quality registries.

CONCLUSIONS

Registries are being developed and used in many high burden countries, but their potential benefits are far from realized as few countries have fully transitioned from paper-based health information to integrated electronic backbone systems. Free tools and frameworks exist to facilitate progress in health information for women and children.