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ObjectiveTo compare neonatal mortality associated with six novel vulnerable newborn types in 125.5 million live births across 15 countries, 2000–2020.DesignPopulation-based, multi-country study.SettingNational data systems in 15 middle- and high-income countries.MethodsWe 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 measuresMortality of six newborn types.ResultsOf 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.ConclusionPreterm 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.

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.

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.

Additional file 1.

Additional file 2.

Additional file 1: Table S1. Univariate analyses of factors associated with incident and recurrent preterm birth in the second pregnancy, 2001-2015 (n = 1,764,025). Table S2. Frequency and factors associated with incident and recurrent preterm birth in the second pregnancy, 2012-2015 (n = 189,227). 1Analysis adjusted for all model variables. 2 Pdifference represents the interaction between incident and recurrent preterm birth for the respective characteristic. The equation for this model is as follows: Logit [ Pr (PTB2=1/Risk factors)] = β0 + β1 (PTB1) + β2 (risk factor X2) + β3 (PTB1) × (risk factor X2), where PTB1 and PTB2 are indicators of a preterm birth in the first and second pregnancy, respectively. Table S3. Incident and recurrent preterm birth in the second pregnancy according to change in the number of prenatal visits between the first and the second pregnancies, 2012-2015 (n = 189,482). 1Adjusted analysis by mother’s residence area, family density, mother’s self-declared race/skin color, mother’s level of education, mother’s marital status, maternal age, type of delivery in the second birth. 2Pdifference represents the interaction between incident and recurrent preterm delivery for the respective characteristic. The equation for this model is as follows: Logit [ Pr (PTB2=1/Risk factors)] = β0 + β1 (PTB1) + β2 (risk factor X2) + β3 (PTB1) × (risk factor X2), where PTB1 and PTB2 are indicators of a preterm birth in the first and second pregnancy, respectively. Table S4. Factors associated with incident and recurrent preterm birth in the second pregnancy, using cross-validation technique, 2001-2015. 1Analysis adjusted for all model variables. Figure S1. ROC curve for logistic regression model of incident preterm birth in cross-validation analysis. (A) Empirical ROC curve; (B) Binomial ROC curve. Non-parametric ROC curve not generated due to database size. Figure S2. ROC curve for logistic regression model of recurrent preterm birth in cross-validation analysis. (A) Empirical ROC curve; (B) Binomial ROC curve; (C) Non-parametric ROC curve.

Additional file 1. The RECORD statement.

Additional file 1: Supplementary Figure S1. Oxford COVID-19 Government Response Tracker Stringency Index in 21 study countries. Supplementary Figure S2. Covid-19 vaccine policies on pregnancy in 18 CoMix countries between March and September 2021. Supplementary Table S1. Number of participants and number of completed surveys by country.

Additional file 1. Table S1. Vaccination plan in Brazil.

OBJECTIVE: To investigate the recurrence of preterm birth (PTB) among the poorest half of the Brazilian population. METHODS: A population-based retrospective study was conducted in Brazil with the live births of multiparous women extracted from the CIDACS Birth Cohort between 2001 and 2015. We used multivariate logistic regression to estimate the odds of recurrent PTB in second and third births. RESULTS: A total of 3 528 050 live births from 1 764 025 multiparous women were analyzed. The adjusted odds for the occurrence of a PTB given a previous PTB was 2.58 (95% confidence interval [CI] 2.53-2.62). Lower gestational age increased the odds of a subsequent PTB (

BACKGROUND: Evidence points to diverse risk factors associated with small- (SGA) and large-for-gestational-age (LGA) births. A more comprehensive understanding of these factors is imperative, especially in vulnerable populations. OBJECTIVES: To estimate the occurrence of and sociodemographic factors associated with SGA and LGA births in poor and extremely poor populations of Brazil. METHODS: The study population consisted of women of reproductive age (14-49 y), whose last child was born between 2012 and 2015. INTERGROWTH 21st consortium criteria were used to classify weight for gestational age according to sex. Multinomial logistic regression modeling was performed to investigate associations of interest. RESULTS: Of 5,521,517 live births analyzed, SGA and LGA corresponded to 7.8% and 17.1%, respectively. Multivariate analysis revealed greater odds of SGA in children born to women who self-reported as black (OR: 1.21; 95% CI: 1.19, 1.22), mixed-race (parda) (OR: 1.08; 95% CI: 1.07, 1.09), or indigenous (OR: 1.11; 95% CI: 1.06, 1.15), were unmarried (OR: 1.08; 95% CI: 1.07, 1.08), illiterate (OR: 1.47; 95% CI: 1.42, 1.52), did not receive prenatal care (OR: 1.57; 95% CI: 1.53, 1.60), or were aged 14-20 y (OR: 1.21; 95% CI: 1.20, 1.22) or 35-49 y (OR: 1.12; 95% CI: 1.10, 1.13). Considering LGA children, higher odds were found in infants born to women living in households with ≥3 inadequate housing conditions (OR: 1.11; 95% CI: 1.10, 1.12), in indigenous women (OR: 1.22; 95% CI: 1.19, 1.25), those who had 1-3 y of schooling (OR: 1.18; 95% CI: 1.17, 1.19), 1-3 prenatal visits (OR: 1.16; CI 95%: 1.14, 1.17), or were older (OR: 1.26; 95% CI: 1.25, 1.27). CONCLUSIONS: In poorer Brazilian populations, socioeconomic, racial, and maternal characteristics are consistently associated with the occurrence of SGA births, but remain less clearly linked to the occurrence of LGA births.

Additional file 1: Table S1. Adjusted models† used to assess factors associated with term low birth weight by year of birth. Table S2. Adjusted models† used to assess factors associated with term low birth weight by exposure time at birth categorized into quartiles intervals.