Small babies, big risks: global estimates of prevalence and mortality for vulnerable newborns to accelerate change and improve counting.

Small newborns are vulnerable to mortality and lifelong loss of human capital. Measures of vulnerability previously focused on liveborn low-birthweight (LBW) babies, yet LBW reduction targets are off-track. There are two pathways to LBW, preterm birth and fetal growth restriction (FGR), with the FGR pathway resulting in the baby being small for gestational age (SGA). Data on LBW babies are available from 158 (81%) of 194 WHO member states and the occupied Palestinian territory, including east Jerusalem, with 113 (58%) having national administrative data, whereas data on preterm births are available from 103 (53%) of 195 countries and areas, with only 64 (33%) providing national administrative data. National administrative data on SGA are available for only eight countries. Global estimates for 2020 suggest 13·4 million livebirths were preterm, with rates over the past decade remaining static, and 23·4 million were SGA. In this Series paper, we estimated prevalence in 2020 for three mutually exclusive types of small vulnerable newborns (SVNs; preterm non-SGA, term SGA, and preterm SGA) using individual-level data (2010-20) from 23 national datasets (∼110 million livebirths) and 31 studies in 18 countries (∼0·4 million livebirths). We found 11·9 million (50% credible interval [Crl] 9·1-12·2 million; 8·8%, 50% Crl 6·8-9·0%) of global livebirths were preterm non-SGA, 21·9 million (50% Crl 20·1-25·5 million; 16·3%, 14·9-18·9%) were term SGA, and 1·5 million (50% Crl 1·2-4·2 million; 1·1%, 50% Crl 0·9-3·1%) were preterm SGA. Over half (55·3%) of the 2·4 million neonatal deaths worldwide in 2020 were attributed to one of the SVN types, of which 73·4% were preterm and the remainder were term SGA. Analyses from 12 of the 23 countries with national data (0·6 million stillbirths at ≥22 weeks gestation) showed around 74% of stillbirths were preterm, including 16·0% preterm SGA and approximately one-fifth of term stillbirths were SGA. There are an estimated 1·9 million stillbirths per year associated with similar vulnerability pathways; hence integrating stillbirths to burden assessments and relevant indicators is crucial. Data can be improved by counting, weighing, and assessing the gestational age of every newborn, whether liveborn or stillborn, and classifying small newborns by the three vulnerability types. The use of these more specific types could accelerate prevention and help target care for the most vulnerable babies.

Neonatal mortality risk of vulnerable newborns by fine stratum of gestational age and birthweight for 230 679 live births in nine low- and middle-income countries, 2000-2017.

OBJECTIVE: To describe the mortality risks by fine strata of gestational age and birthweight among 230 679 live births in nine low- and middle-income countries (LMICs) from 2000 to 2017. DESIGN: Descriptive multi-country secondary data analysis. SETTING: Nine LMICs in sub-Saharan Africa, Southern and Eastern Asia, and Latin America. POPULATION: Liveborn infants from 15 population-based cohorts. METHODS: Subnational, population-based studies with high-quality birth outcome data were invited to join the Vulnerable Newborn Measurement Collaboration. All studies included birthweight, gestational age measured by ultrasound or last menstrual period, infant sex and neonatal survival. We defined adequate birthweight as 2500-3999 g (reference category), macrosomia as ≥4000 g, moderate low as 1500-2499 g and very low birthweight as <1500 g. We analysed fine strata classifications of preterm, term and post-term: ≥42+0 , 39+0 -41+6 (reference category), 37+0 -38+6 , 34+0 -36+6 ,34+0 -36+6 ,32+0 -33+6 , 30+0 -31+6 , 28+0 -29+6 and less than 28 weeks. MAIN OUTCOME MEASURES: Median and interquartile ranges by study for neonatal mortality rates (NMR) and relative risks (RR). We also performed meta-analysis for the relative mortality risks with 95% confidence intervals (CIs) by the fine categories, stratified by regional study setting (sub-Saharan Africa and Southern Asia) and study-level NMR (≤25 versus >25 neonatal deaths per 1000 live births). RESULTS: We found a dose-response relationship between lower gestational ages and birthweights with increasing neonatal mortality risks. The highest NMR and RR were among preterm babies born at <28 weeks (median NMR 359.2 per 1000 live births; RR 18.0, 95% CI 8.6-37.6) and very low birthweight (462.8 per 1000 live births; RR 43.4, 95% CI 29.5-63.9). We found no statistically significant neonatal mortality risk for macrosomia (RR 1.1, 95% CI 0.6-3.0) but a statistically significant risk for all preterm babies, post-term babies (RR 1.3, 95% CI 1.1-1.5) and babies born at 370 -386 weeks (RR 1.2, 95% CI 1.0-1.4). There were no statistically significant differences by region or underlying neonatal mortality. CONCLUSIONS: In addition to tracking vulnerable newborn types, monitoring finer categories of birthweight and gestational age will allow for better understanding of the predictors, interventions and health outcomes for vulnerable newborns. It is imperative that all newborns from live births and stillbirths have an accurate recorded weight and gestational age to track maternal and neonatal health and optimise prevention and care of vulnerable newborns.

Delivery and postnatal care among women in 71 low- and middle-income countries: analyzing coverage gaps using household surveys.

BACKGROUND: High levels of maternal morbidity and mortality persist in low- and middle-income countries, despite increases in coverage of facility delivery and skilled assistance at delivery. We compared levels of facility birth to a summary delivery care measure and quantified gaps. METHODS: We approximated a delivery care score from type of delivery (home, lower-level facility, or hospital), skilled attendant at delivery, a stay of 24-or-more-hours after delivery, and a health check within 48-h after delivery. Data were obtained from 333,316 women aged 15-49 who had a live birth in the previous 2 years, and from 71 countries with nationally representative surveys between 2013 and 2020. We computed facility delivery and delivery care coverage estimates to assess the gap. We stratified the analysis by country characteristics, including the national maternal mortality ratio (MMR), to assess the size of coverage gaps, and we assessed missed opportunities through coverage cascades. We looked at the association between MMR and delivery care coverage. RESULTS: Delivery care coverage varied by country, ranging from 24% in Sudan to 100% in Cuba. Median coverage was 70% with an interquartile range of 30 percentage points (55% and 85%). The cascade showed that while 76% of women delivered in a facility, only 41% received all four interventions. Coverage gaps exist across all MMR levels. Gaps between highest and lowest wealth quintiles were greatest in countries with MMR levels of 100 or higher, and the gap narrowed in countries with MMR levels below 100. The delivery care indicator had a negative association with MMR. CONCLUSIONS: In addition to providing high-quality evidenced-based care to women during birth and the postpartum period, there is also a need to address gaps in delivery care, which occur within and between countries, wealth quintiles, and MMR phases.

Climate shocks and nutrition: The role of food security policies and programs in enhancing maternal and neonatal survival in Niger.

Niger is afflicted with high rates of poverty, high fertility rates, frequent environmental crises, and climate change. Recurrent droughts and floods have led to chronic food insecurity linked to poor maternal and neonatal nutrition outcomes in vulnerable regions. We analyzed maternal and neonatal nutrition trends and subnational variability between 2000 and 2021 with a focus on the implementation of policies and programs surrounding two acute climate shocks in 2005 and 2010. We used four sources of data: (a) national household surveys for maternal and newborn nutritional indicators allowing computation of trends and differences at national and regional levels; (b) document review of food security reports; (c) 30 key informant interviews and; (d) one focus group discussion. Many food security policies and nutrition programs were enacted from 2000 to 2020. Gains in maternal and neonatal nutrition indicators were more significant in targeted vulnerable regions of Maradi, Zinder, Tahoua and Tillabéri, from 2006 to 2021. However, poor access to financial resources for policy execution and suboptimal implementation of plans have hindered progress. In response to the chronic climate crisis over the last 20 years, the Nigerien government and program implementers have demonstrated their commitment to reducing food insecurity and enhancing resilience to climate shocks by adopting a deliberate multisectoral effort. However, there is more that can be achieved with a continued focus on vulnerable regions to build resilience, targeting high risk populations, and investing in infrastructure to improve health systems, food systems, agriculture systems, education systems, and social protection.

Neonatal mortality risk of large-for-gestational-age and macrosomic live births in 15 countries, including 115.6 million nationwide linked records, 2000-2020.

OBJECTIVE: We aimed to compare the prevalence and neonatal mortality associated with large for gestational age (LGA) and macrosomia among 115.6 million live births in 15 countries, between 2000 and 2020. DESIGN: Population-based, multi-country study. SETTING: National healthcare systems. POPULATION: Liveborn infants. METHODS: We used individual-level data identified for the Vulnerable Newborn Measurement Collaboration. We calculated the prevalence and relative risk (RR) of neonatal mortality among live births born at term + LGA (>90th centile, and also >95th and >97th centiles when the data were available) versus term + appropriate for gestational age (AGA, 10th-90th centiles) and macrosomic (≥4000, ≥4500 and ≥5000 g, regardless of gestational age) versus 2500-3999 g. INTERGROWTH 21st served as the reference population. MAIN OUTCOME MEASURES: Prevalence and neonatal mortality risks. RESULTS: Large for gestational age was common (median prevalence 18.2%; interquartile range, IQR, 13.5%-22.0%), and overall was associated with a lower neonatal mortality risk compared with AGA (RR 0.83, 95% CI 0.77-0.89). Around one in ten babies were ≥4000 g (median prevalence 9.6% (IQR 6.4%-13.3%), with 1.2% (IQR 0.7%-2.0%) ≥4500 g and with 0.2% (IQR 0.1%-0.2%) ≥5000 g). Overall, macrosomia of ≥4000 g was not associated with increased neonatal mortality risk (RR 0.80, 95% CI 0.69-0.94); however, a higher risk was observed for birthweights of ≥4500 g (RR 1.52, 95% CI 1.10-2.11) and ≥5000 g (RR 4.54, 95% CI 2.58-7.99), compared with birthweights of 2500-3999 g, with the highest risk observed in the first 7 days of life. CONCLUSIONS: In this population, birthweight of ≥4500 g was the most useful marker for early mortality risk in big babies and could be used to guide clinical management decisions.

Neonatal mortality risk of vulnerable newborns: A descriptive analysis of subnational, population-based birth cohorts for 238 203 live births in low- and middle-income settings from 2000 to 2017.

OBJECTIVE: We aimed to understand the mortality risks of vulnerable newborns (defined as preterm and/or born weighing smaller or larger compared to a standard population), in low- and middle-income countries (LMICs). DESIGN: Descriptive multi-country, secondary analysis of individual-level study data of babies born since 2000. SETTING: Sixteen subnational, population-based studies from nine LMICs in sub-Saharan Africa, Southern and Eastern Asia, and Latin America. POPULATION: Live birth neonates. METHODS: We categorically defined five vulnerable newborn types based on size (large- or appropriate- or small-for-gestational age [LGA, AGA, SGA]), and term (T) and preterm (PT): T + LGA, T + SGA, PT + LGA, PT + AGA, and PT + SGA, with T + AGA (reference). A 10-type definition included low birthweight (LBW) and non-LBW, and a four-type definition collapsed AGA/LGA into one category. We performed imputation for missing birthweights in 13 of the studies. MAIN OUTCOME MEASURES: Median and interquartile ranges by study for the prevalence, mortality rates and relative mortality risks for the four, six and ten type classification. RESULTS: There were 238 203 live births with known neonatal status. Four of the six types had higher mortality risk: T + SGA (median relative risk [RR] 2.6, interquartile range [IQR] 2.0-2.9), PT + LGA (median RR 7.3, IQR 2.3-10.4), PT + AGA (median RR 6.0, IQR 4.4-13.2) and PT + SGA (median RR 10.4, IQR 8.6-13.9). T + SGA, PT + LGA and PT + AGA babies who were LBW, had higher risk compared with non-LBW babies. CONCLUSIONS: Small and/or preterm babies in LIMCs have a considerably increased mortality risk compared with babies born at term and larger. This classification system may advance the understanding of the social determinants and biomedical risk factors along with improved treatment that is critical for newborn health.

Psychosocial Assessment Tools for Youth with Type 1 Diabetes: a 10-Year Review.

PURPOSE OF REVIEW: There is a notable lack of consistency in the measurement of psychosocial factors affecting youth with type 1 diabetes, resulting in a need for increased measurement standardization and establishment of measures tailored to capture unique experiences faced by youth. This review sought to assess 10 years of extant literature (2011 to 2020) to identify which established measurement tools are commonly used and to evaluate new measurement tools that were introduced during this period. RECENT FINDINGS: There are a variety of psychosocial factors affecting youth, and assessment of these measures has shown substantial variability. Our review found that most frequently cited scales were those pertaining to self-efficacy, diabetes distress, family conflict, autonomy, and fear of hypoglycemia. During our review period, experts developed and validated 21 new scales, the majority of which sought to evaluate areas pertaining to diabetes distress. Of the common scales and newly developed scales identified in this review, psychometric properties showcase high reliability and validity, and items are becoming increasingly specific to youth but still lack assessment of how youth perceive technology's impact on diabetes management. The field would benefit from measures employing more nuanced age specificity and addressing technology usage.

Demographic, socio-economic, obstetric, and behavioral factors associated with small-and large-for-gestational-age from a prospective, population-based pregnancy cohort in rural Nepal: a secondary data analysis.

BACKGROUND: In South Asia, a third of babies are born small-for-gestational age (SGA). The risk factors are well described in the literature, but many studies are in high-and-middle income countries or measure SGA on facility births only. There are fewer studies that describe the prevalence of risk factors for large-for-gestational age (LGA) in low-income countries. We aim to describe the factors associated with SGA and LGA in a population-based cohort of pregnant women in rural Nepal. METHODS: This is a secondary data analysis of community-based trial on neonatal oil massage (22,545 women contributing 39,479 pregnancies). Demographic, socio-economic status (SES), medical/obstetric history, and timing of last menstruation were collected at enrollment. Vital signs, illness symptoms, and antenatal care (ANC) attendance were collected throughout the pregnancy and neonatal weight was measured for live births. We conducted multivariate analysis using multinomial, multilevel logistic regression, reporting the odds ratio (OR) with 95% confidence intervals (CIs). Outcomes were SGA, LGA compared to appropriate-for-gestational age (AGA) and were multiply imputed using birthweight recalibrated to time at delivery. RESULTS: SGA was associated with nulligravida (OR: 2.12 95% CI: 1.93-2.34), gravida/nulliparous (OR: 1.86, 95% CI: 1.26-2.74), interpregnancy intervals less than 18 months (OR: 1.16, 95% CI: 1.07-1.27), and poor appetite/vomiting in the second trimester, (OR: 1.27, 95% CI: 1.19-1.35). Greater wealth (OR: 0.78, 95% CI: 0.69-0.88), swelling of hands/face in the third trimester (OR: 0.81, 95% CI: 0.69-0.94) parity greater than five (OR: 0.77, 95% CI: 0.65-0.92), male fetal sex (OR: 0.91, 95% CI: 0.86-0.98), and increased weight gain (OR: 0.93 per weight kilogram difference between 2nd and 3rd trimester, 95% CI: 0.92-0.95) were protective for SGA. Four or more ANC visits (OR: 0.53, 95% CI: 0.41-0.68) and respiratory symptoms in the third trimester (OR: 0.67, 95% CI: 0.54-0.84) were negatively associated with LGA, and maternal age < 18 years (OR: 1.39, 95% CI: 1.03-1.87) and respiratory symptoms in the second trimester (OR: 1.27, 95% CI: 1.07-1.51) were positively associated with LGA. CONCLUSIONS: Our findings are in line with known risk factors for SGA. Because the prevalence and mortality risk of LGA babies is low in this population, it is likely LGA status does not indicate underlaying illness. Improved and equitable access to high quality antenatal care, monitoring for appropriate gestational weight gain and increased monitoring of women with high-risk pregnancies may reduce prevalence and improve outcomes of SGA babies. TRIAL REGISTRATION: The study used in this secondary data analysis was registered at Clinicaltrials.gov NCT01177111.

Barriers and facilitators to influenza and pneumococcal vaccine hesitancy in rheumatoid arthritis: a qualitative study.

OBJECTIVES: Immunization is an essential component of RA care. Nevertheless, vaccine coverage in RA is suboptimal. Contextual, individual and vaccine-related factors influence vaccine acceptance. However, barriers and facilitators of vaccination in RA are not well defined. The aim of this study was to assess perspectives of RA patients and healthcare professionals (HCPs) involved in RA care of barriers and facilitators regarding influenza and pneumococcal vaccines. METHODS: Eight focus groups (four with RA patients and four with HCPs) and eight semi-structured open-ended individual interviews with vaccine-hesitant RA patients were conducted. Data were audio recorded, transcribed verbatim and imported to MAXQDA software. Analysis using the framework of vaccine hesitancy proposed by the Strategic Advisory Group of Experts on Immunization was conducted. RESULTS: RA patients and HCPs reported common and specific barriers and facilitators to influenza vaccination that included contextual, individual and/or group and vaccine- and/or vaccination-specific factors. A key contextual influence on vaccination was patients' perception of the media, pharmaceutical industry, authorities, scientists and the medical community at large. Among the individual-related influences, experiences with vaccination, knowledge/awareness and beliefs about health and disease prevention were considered to impact vaccine acceptance. Vaccine-related factors including concerns about vaccine side effects such as RA flares, the safety of new formulations, the mechanism of action, access to vaccines and costs associated with vaccination were identified as actionable barriers. CONCLUSION: Acknowledging RA patients' perceived barriers to influenza and pneumococcal vaccination and implementing specific strategies to address them might increase vaccination coverage in this population.

Estimating population-based coverage of reproductive, maternal, newborn, and child health (RMNCH) interventions from health management information systems: a comprehensive review.

BACKGROUND: Routinely collected health facility data usually captured and stored in Health Management Information Systems (HMIS) are potential sources of data for frequent and local disaggregated estimation of the coverage of reproductive, maternal, newborn, and child health interventions (RMNCH), but have been under-utilized due to concerns over data quality. We reviewed methods for estimation of national or subnational coverage of RMNCH interventions using HMIS data exclusively or in conjunction with survey data from low- and middle-income countries (LMICs). METHODS: We conducted a comprehensive review of studies indexed in PubMed and Scopus to identify potential papers based on predefined search terms. Two reviewers screened the papers using defined inclusion and exclusion criteria. Following sequences of title, abstract and full paper reviews, we retained 18 relevant papers. RESULTS: 12 papers used only HMIS data and 6 used both HMIS and survey data. There is enormous lack of standards in the existing methods for estimating RMNCH intervention coverage; all appearing to be highly author dependent. The denominators for coverage measures were estimated using census, non-census and combined projection-based methods. No satisfactory methods were found for treatment-based coverage indicators for which the estimation of target population requires the population prevalence of underlying conditions. The estimates of numerators for the coverage measures were obtained from the count of users or visits and in some cases correction for completeness of reporting in the HMIS following an assessment of data quality. CONCLUSIONS: Standard methods for correcting numerators from HMIS data for accurate estimation of coverage of RMNCH interventions are needed to expand the use of these data. More research and investments are required to improve denominators for health facility-derived statistics. Improvement in routine data quality and analytical methods would allow for timely estimation of RMNCH intervention coverage at the national and subnational levels.