Vulnerable newborn types: Analysis of population-based registries for 165 million births in 23 countries, 2000-2021

OBJECTIVE

To examine the prevalence of novel newborn types among 165 million live births in 23 countries from 2000 to 2021.

DESIGN

Population-based, multi-country analysis.

SETTING

National data systems in 23 middle- and high-income countries.

POPULATION

Liveborn infants.

METHODS

Country teams with high-quality data were invited to be part of the Vulnerable Newborn Measurement Collaboration. We classified live births by six newborn types based on gestational age information (preterm <37 weeks versus term ≥37 weeks) and size for gestational age defined as small (SGA, <10th centile), appropriate (10th-90th centiles), or large (LGA, >90th centile) for gestational age, according to INTERGROWTH-21st standards. We considered small newborn types of any combination of preterm or SGA, and term + LGA was considered large. Time trends were analysed using 3-year moving averages for small and large types.

MAIN OUTCOME MEASURES

Prevalence of six newborn types.

RESULTS

We analysed 165 017 419 live births and the median prevalence of small types was 11.7% - highest in Malaysia (26%) and Qatar (15.7%). Overall, 18.1% of newborns were large (term + LGA) and was highest in Estonia 28.8% and Denmark 25.9%. Time trends of small and large infants were relatively stable in most countries.

CONCLUSIONS

The distribution of newborn types varies across the 23 middle- and high-income countries. Small newborn types were highest in west Asian countries and large types were highest in Europe. To better understand the global patterns of these novel newborn types, more information is needed, especially from low- and middle-income countries.

My Baby's Movements: An assessment of the effectiveness of the My Baby's Movements phone program in reducing late-gestation stillbirth rates

BACKGROUND

Delayed reporting of decreased fetal movements (DFM) could represent a missed opportunity to prevent stillbirth. Mobile phone applications (apps) have the potential to improve maternal awareness and reporting of DFM and contribute to stillbirth prevention.

AIMS

To evaluate the effectiveness of the My Baby's Movements (MBM) app on late-gestation stillbirth rates.

MATERIALS AND METHODS

The MBM trial evaluated a multifaceted fetal movements awareness package across 26 maternity services in Australia and New Zealand between 2016 and 2019. In this secondary analysis, generalised linear mixed models were used to compare rates of late-gestation stillbirth, obstetric interventions, and neonatal outcomes between app users and non-app users including calendar time, cluster, primiparity and other potential confounders as fixed effects, and hospital as a random effect.

RESULTS

Of 140 052 women included, app users comprised 9.8% (n = 13 780). The stillbirth rate was not significantly lower among app users (1.67/1000 vs 2.29/1000) (adjusted odds ratio (aOR) 0.79; 95% CI 0.51-1.23). App users were less likely to have a preterm birth (aOR 0.81; 0.75-0.88) or a composite adverse neonatal outcome (aOR 0.87; 0.81-0.93); however, they had higher rates of induction of labour (IOL) (aOR 1.27; 1.22-1.32) and early term birth (aOR 1.08; 1.04-1.12).

CONCLUSIONS

The MBM app had low uptake and its use was not associated with stillbirth rates but was associated with some neonatal benefit, and higher rates of IOL and early term birth. Use and acceptability of tools designed to promote fetal movement awareness is an important knowledge gap. The implications of increased IOL and early term births warrant consideration in future studies.

Evaluation of Movements Matter: A social media and hospital-based campaign aimed at raising awareness of decreased fetal movements

BACKGROUND

The Movements Matter campaign aimed to raise awareness of decreased fetal movements (DFM) among pregnant women and inform clinicians of best practice management.

AIM

To conduct a process evaluation of campaign implementation, and an impact evaluation of the campaign's effects on knowledge and experiences of pregnant women, and attitudes and practices of clinicians in relation to DFM.

METHODS

This study used a cross-sectional before-after design. Pregnant women and clinicians were sampled at five hospitals. Women were surveyed about their knowledge of DFM, and actions to take if they noticed DFM. Clinicians were asked about their current practices and attitudes about informing women about DFM. Logistic regression was used to calculate campaign effects on outcome measures.

RESULTS

The Movements Matter campaign reached 653 262 people on social media, as well as being covered on news media and popular women's websites. The evaluation surveyed 1142 pregnant women pre-campaign and 473 post-campaign, and 372 clinicians pre-campaign and 149 post-campaign. Following the campaign, women were more likely to be aware that babies should move the same amount in late pregnancy (adjusted odds ratio (aOR) 1.81, 95% CI 1.43-2.27), and were more likely to contact their health service immediately if their baby was moving less (aOR 1.52, 95% CI 1.22-1.91). Clinicians were 2.84 times more likely to recommend women should come in for assessment if they experience DFM (95% CI 1.35-5.97).

CONCLUSIONS

This evaluation has shown that a campaign using social media and in-hospital education materials led to some increases in knowledge about fetal movements among pregnant women.

Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia

BACKGROUND

Despite advances in the care of women and their babies in the past century, an estimated 1.7 million babies are born still each year throughout the world. A robust method to estimate a pregnant woman's individualized risk of late-pregnancy stillbirth is needed to inform decision-making around the timing of birth to reduce the risk of stillbirth from 35 weeks of gestation in Australia, a high-resource setting.

METHODS

This is a protocol for a cross-sectional study of all late-pregnancy births in Australia (2005-2015) from 35 weeks of gestation including 5188 stillbirths among 3.1 million births at an estimated rate of 1.7 stillbirths per 1000 births. A multivariable logistic regression model will be developed in line with current Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) guidelines to estimate the gestation-specific probability of stillbirth with prediction intervals. Candidate predictors were identified from systematic reviews and clinical consultation and will be described through univariable regression analysis. To generate a final model, elimination by backward stepwise multivariable logistic regression will be performed. The model will be internally validated using bootstrapping with 1000 repetitions and externally validated using a temporally unique dataset. Overall model performance will be assessed with R², calibration, and discrimination. Calibration will be reported using a calibration plot with 95% confidence intervals (α = 0.05). Discrimination will be measured by the C-statistic and area underneath the receiver-operator curves. Clinical usefulness will be reported as positive and negative predictive values, and a decision curve analysis will be considered.

DISCUSSION

A robust method to predict a pregnant woman's individualized risk of late-pregnancy stillbirth is needed to inform timely, appropriate care to reduce stillbirth. Among existing prediction models designed for obstetric use, few have been subject to internal and external validation and many fail to meet recommended reporting standards. In developing a risk prediction model for late-gestation stillbirth with both providers and pregnant women in mind, we endeavor to develop a validated model for clinical use in Australia that meets current reporting standards.