Socioeconomic disparities in changes to preterm birth and stillbirth rates during the first year of the COVID-19 pandemic: a study of 21 European countries

Background: Despite concerns about worsening pregnancy outcomes resulting from healthcare restrictions, economic difficulties and increased stress during the COVID-19 pandemic, preterm birth (PTB) rates declined in some countries in 2020, while stillbirth rates appeared stable. Like other shocks, the pandemic may have exacerbated existing socioeconomic disparities in pregnancy, but this remains to be established. Our objective was to investigate changes in PTB and stillbirth by socioeconomic status (SES) in European countries. Methods: The Euro-Peristat network implemented this study within the Population Health Information Research Infrastructure (PHIRI) project. A common data model was developed to collect aggregated tables from routine birth data for 2015-2020. SES was based on mother's educational level or area-level deprivation/maternal occupation if education was unavailable and harmonized into low, medium and high SES. Country-specific relative risks (RRs) of PTB and stillbirth for March to December 2020, adjusted for linear trends from 2015 to 2019, by SES group were pooled using random effects meta-analysis. Results: Twenty-one countries provided data on perinatal outcomes by SES. PTB declined by an average 4% in 2020 {pooled RR: 0.96 [95% confidence intervals (CIs): 0.94-0.97]} with similar estimates across all SES groups. Stillbirths rose by 5% [RR: 1.05 (95% CI: 0.99-1.10)], with increases of between 3 and 6% across the three SES groups, with overlapping confidence limits. Conclusions: PTB decreases were similar regardless of SES group, while stillbirth rates rose without marked differences between groups.This research was financially supported by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No. 101018317 (Population Health Information Research Infrastructure [PHIRI])

Neonatal mortality risk for vulnerable newborn types in 15 countries using 125.5 million nationwide birth outcome records, 2000-2020.

OBJECTIVE: To compare neonatal mortality associated with six novel vulnerable newborn types in 125.5 million live births across 15 countries, 2000-2020. DESIGN: Population-based, multi-country study. SETTING: National data systems in 15 middle- and high-income countries. METHODS: We used individual-level data sets identified for the Vulnerable Newborn Measurement Collaboration. We examined the contribution to neonatal mortality of six newborn types combining gestational age (preterm [PT] versus term [T]) and size-for-gestational age (small [SGA], 90th centile) according to INTERGROWTH-21st newborn standards. Newborn babies with PT or SGA were defined as small and T + LGA was considered as large. We calculated risk ratios (RRs) and population attributable risks (PAR%) for the six newborn types. MAIN OUTCOME MEASURES: Mortality of six newborn types. RESULTS: Of 125.5 million live births analysed, risk ratios were highest among PT + SGA (median 67.2, interquartile range [IQR] 45.6-73.9), PT + AGA (median 34.3, IQR 23.9-37.5) and PT + LGA (median 28.3, IQR 18.4-32.3). At the population level, PT + AGA was the greatest contributor to newborn mortality (median PAR% 53.7, IQR 44.5-54.9). Mortality risk was highest among newborns born before 28 weeks (median RR 279.5, IQR 234.2-388.5) compared with babies born between 37 and 42 completed weeks or with a birthweight less than 1000 g (median RR 282.8, IQR 194.7-342.8) compared with those between 2500 g and 4000 g as a reference group. CONCLUSION: Preterm newborn types were the most vulnerable, and associated with the highest mortality, particularly with co-existence of preterm and SGA. As PT + AGA is more prevalent, it is responsible for the greatest burden of neonatal deaths at population level

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 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

Using tactile-exploration with the unscented kalman filter for high precision on-line shape and pose estimation of a 3D workpiece

A common problem faced in robotic manipulation is developing techniques for accurate lo- calisation and mapping of 3D objects. Many techniques already exist to aid in estimating the structure of the world using information from a robot's sensors such as stereo cameras, time-of-ight or structured light. These sensors and techniques used for modelling can of-Ten be made accurate enough for most practical applications (such as picking-up an object). However, some applications require a higher degree of accuracy (sub-millimeter) that is difficult to achieve with the information available from these sensors. This paper proposes the use of tactile exploration to incrementally i prove the accuracy of a prior 3D object model as the robot touches different parts of a work- piece. A modified Unscented Kalman Filter (UKF) has been developed to fuse the touch probe data with the existing model and refine it over time. The approach presented in this paper is intended for applications that require a high degree of accuracy and reliability (such as medical procedures) and as such, focuses on three primary requirements|accuracy, robustness and practicality