National, regional, and global estimates of low birthweight in 2020, with trends from 2000: a systematic analysis.

BACKGROUND: Low birthweight (LBW; <2500 g) is an important predictor of health outcomes throughout the life course. We aimed to update country, regional, and global estimates of LBW prevalence for 2020, with trends from 2000, to assess progress towards global targets to reduce LBW by 30% by 2030. METHODS: For this systematic analysis, we searched population-based, nationally representative data on LBW from Jan 1, 2000, to Dec 31, 2020. Using 2042 administrative and survey datapoints from 158 countries and areas, we developed a Bayesian hierarchical regression model incorporating country-specific intercepts, time-varying covariates, non-linear time trends, and bias adjustments based on data quality. We also provided novel estimates by birthweight subgroups. FINDINGS: An estimated 19·8 million (95% credible interval 18·4-21·7 million) or 14·7% (13·7-16·1) of liveborn newborns were LBW worldwide in 2020, compared with 22·1 million (20·7-23·9 million) and 16·6% (15·5-17·9) in 2000-an absolute reduction of 1·9 percentage points between 2000 and 2020. Using 2012 as the baseline, as this is when the Global Nutrition Target began, the estimated average annual rate of reduction from 2012 to 2020 was 0·3% worldwide, 0·85% in southern Asia, and 0·59% in sub-Saharan Africa. Nearly three-quarters of LBW births in 2020 occurred in these two regions: of 19 833 900 estimated LBW births worldwide, 8 817 000 (44·5%) were in southern Asia and 5 381 300 (27·1%) were in sub-Saharan Africa. Of 945 300 estimated LBW births in northern America, Australia and New Zealand, central Asia, and Europe, approximately 35·0% (323 700) weighed less than 2000 g: 5·8% (95% CI 5·2-6·4; 54 800 [95% CI 49 400-60 800]) weighed less than 1000 g, 9·0% (8·7-9·4; 85 400 [82 000-88 900]) weighed between 1000 g and 1499 g, and 19·4% (19·0-19·8; 183 500 [180 000-187 000]) weighed between 1500 g and 1999 g. INTERPRETATION: Insufficient progress has occurred over the past two decades to meet the Global Nutrition Target of a 30% reduction in LBW between 2012 and 2030. Accelerating progress requires investments throughout the lifecycle focused on primary prevention, especially for adolescent girls and women living in the most affected countries. With increasing numbers of births in facilities and advancing electronic information systems, improvements in the quality and availability of administrative LBW data are also achievable. FUNDING: The Children's Investment Fund Foundation; the UNDP-UNFPA-UNICEF-WHO World Bank Special Programme of Research, Development and Research Training in Human Reproduction; and the Bill & Melinda Gates Foundation.

National, regional, and global estimates of preterm birth in 2020, with trends from 2010: a systematic analysis.

BACKGROUND: Preterm birth is the leading cause of neonatal mortality and is associated with long-term physical, neurodevelopmental, and socioeconomic effects. This study updated national preterm birth rates and trends, plus novel estimates by gestational age subgroups, to inform progress towards global health goals and targets, and aimed to update country, regional, and global estimates of preterm birth for 2020 in addition to trends between 2010 and 2020. METHODS: We systematically searched population-based, nationally representative data on preterm birth from Jan 1, 2010, to Dec 31, 2020 and study data (26 March-14 April, 2021) for countries and areas with no national-level data. The analysis included 679 data points (86% nationally representative administrative data [582 of 679 data points]) from 103 countries and areas (62% of countries and areas having nationally representative administrative data [64 of 103 data points]). A Bayesian hierarchical regression was used for estimating country-level preterm rates, which incoporated country-specific intercepts, low birthweight as a covariate, non-linear time trends, and bias adjustments based on a data quality categorisation, and other indicators such as method of gestational age estimation. FINDINGS: An estimated 13·4 million (95% credible interval [CrI] 12·3-15·2 million) newborn babies were born preterm (<37 weeks) in 2020 (9·9% of all births [95% CrI 9·1-11·2]) compared with 13·8 million (12·7-15·5 million) in 2010 (9·8% of all births [9·0-11·0]) worldwide. The global annual rate of reduction was estimated at -0·14% from 2010 to 2020. In total, 55·6% of total livebirths are in southern Asia (26·8% [36 099 000 of 134 767 000]) and sub-Saharan Africa (28·7% [38 819 300 of 134 767 000]), yet these two regions accounted for approximately 65% (8 692 000 of 13 376 200) of all preterm births globally in 2020. Of the 33 countries and areas in the highest data quality category, none were in southern Asia or sub-Saharan Africa compared with 94% (30 of 32 countries) in high-income countries and areas. Worldwide from 2010 to 2020, approximately 15% of all preterm births occurred at less than 32 weeks of gestation, requiring more neonatal care (<28 weeks: 4·2%, 95% CI 3·1-5·0, 567 800 [410 200-663 200 newborn babies]); 28-32 weeks: 10·4% [9·5-10·6], 1 392 500 [1 274 800-1 422 600 newborn babies]). INTERPRETATION: There has been no measurable change in preterm birth rates over the last decade at global level. Despite increasing facility birth rates and substantial focus on routine health data systems, there remain many missed opportunities to improve preterm birth data. Gaps in national routine data for preterm birth are most marked in regions of southern Asia and sub-Saharan Africa, which also have the highest estimated burden of preterm births. Countries need to prioritise programmatic investments to prevent preterm birth and to ensure evidence-based quality care when preterm birth occurs. Investments in improving data quality are crucial so that preterm birth data can be improved and used for action and accountability processes. FUNDING: The Children's Investment Fund Foundation and the UNDP, United Nations Population Fund-UNICEF-WHO-World Bank Special Programme of Research, Development and Research Training in Human Reproduction.

Natural History of Malaria Infections During Early Childhood in Twins.

BACKGROUND: The frequency and clinical presentation of malaria infections show marked heterogeneity in epidemiological studies. However, deeper understanding of this variability is hampered by the difficulty in quantifying all relevant factors. Here, we report the history of malaria infections in twins, who are exposed to the same in utero milieu, share genetic factors, and are similarly exposed to vectors. METHODS: Data were obtained from a Malian longitudinal birth cohort. Samples from 25 twin pairs were examined for malaria infection and antibody responses. Bayesian models were developed for the number of infections during follow-up. RESULTS: In 16 of 25 pairs, both children were infected and often developed symptoms. In 8 of 25 pairs, only 1 twin was infected, but usually only once or twice. Statistical models suggest that this pattern is not inconsistent with twin siblings having the same underlying infection rate. In a pair with discordant hemoglobin genotype, parasite densities were consistently lower in the child with hemoglobin AS, but antibody levels were similar. CONCLUSIONS: By using a novel design, we describe residual variation in malaria phenotypes in naturally matched children and confirm the important role of environmental factors, as suggested by the between-twin pair heterogeneity in malaria history.

Estimation of country-level incidence of early-onset invasive Group B Streptococcus disease in infants using Bayesian methods.

Neonatal invasive disease caused by Group B Streptococcus (GBS) is responsible for much acute mortality and long-term morbidity. To guide development of better prevention strategies, including maternal vaccines that protect neonates against GBS, it is necessary to estimate the burden of this condition globally and in different regions. Here, we present a Bayesian model that estimates country-specific invasive GBS (iGBS) disease incidence in children aged 0 to 6 days. The model combines different types of epidemiological data, each of which has its own limitations: GBS colonization prevalence in pregnant women, risk of iGBS disease in children born to GBS-colonized mothers and direct estimates of iGBS disease incidence where available. In our analysis, we present country-specific maternal GBS colonization prevalence after adjustment for GBS detection assay used in epidemiological studies. We then integrate these results with other epidemiological data and estimate country-level incidence of iGBS disease including in countries with no studies that directly estimate incidence. We are able to simultaneously estimate two key epidemiological quantities: the country-specific incidence of early-onset iGBS disease, and the risk of iGBS disease in babies born to GBS-colonized women. Overall, we believe our method will contribute to a more comprehensive quantification of the global burden of this disease, inform cost-effectiveness assessments of potential maternal GBS vaccines and identify key areas where data are necessary.

Understanding the complexity of glycaemic health: systematic bio-psychosocial modelling of fasting glucose in middle-age adults; a DynaHEALTH study.

BACKGROUND: The prevention of the risk of type 2 diabetes (T2D) is complicated by multidimensional interplays between biological and psychosocial factors acting at the individual level. To address the challenge we took a systematic approach, to explore the bio-psychosocial predictors of blood glucose in mid-age. METHODS: Based on the 31-year and 46-year follow-ups (5,078 participants, 43% male) of Northern Finland Birth Cohort 1966, we used a systematic strategy to select bio-psychosocial variables at 31 years to enable a data-driven approach. As selection criteria, the variable must be (i) a component of the metabolic syndrome or an indicator of psychosocial health using WHO guidelines, (ii) easily obtainable in general health check-ups and (iii) associated with fasting blood glucose at 46 years (P < 0.10). Exploratory and confirmatory factor analysis were used to derive latent factors, and stepwise linear regression allowed exploration of relationships between factors and fasting glucose. RESULTS: Of all 26 variables originally considered, 19 met the selection criteria and were included in an exploratory factor analysis. Two variables were further excluded due to low loading (<0.3). We derived four latent factors, which we named as socioeconomic, metabolic, psychosocial and blood pressure status. The combination of metabolic and psychosocial factors, adjusted for sex, provided best prediction of fasting glucose at 46 years (explaining 10.7% of variation in glucose; P < 0.001). Regarding different bio-psychosocial pathways and relationships, the importance of psychosocial factors in addition to established metabolic risk factors was highlighted. CONCLUSIONS: The present study supports evidence for the bio-psychosocial nature of adult glycemic health and exemplifies an evidence-based approach to model the bio-psychosocial relationships. The factorial model may help further research and public health practice in focusing also on psychosocial aspects in maintaining normoglycaemia in the prevention of cardio-metabolic diseases.

A comparative study of RNA-seq analysis strategies.

Three principal approaches have been proposed for inferring the set of transcripts expressed in RNA samples using RNA-seq. The simplest approach uses curated annotations, which assumes the transcripts in a sample are a subset of the transcripts listed in a curated database. A more ambitious method involves aligning reads to a reference genome and using the alignments to infer the transcript structures, possibly with the aid of a curated transcript database. The most challenging approach is to assemble reads into putative transcripts de novo without the aid of reference data. We have systematically assessed the properties of these three approaches through a simulation study. We have found that the sensitivity of computational transcript set estimation is severely limited. Computational approaches (both genome-guided and de novo assembly) produce a large number of artefacts, which are assigned large expression estimates and absorb a substantial proportion of the signal when performing expression analysis. The approach using curated annotations shows good expression correlation even when the annotations are incomplete. Furthermore, any incorrect transcripts present in a curated set do not absorb much signal, so it is preferable to have a curation set with high sensitivity than high precision. Software to simulate transcript sets, expression values and sequence reads under a wider range of parameter values and to compare sensitivity, precision and signal-to-noise ratios of different methods is freely available online (https://github.com/boboppie/RSSS) and can be expanded by interested parties to include methods other than the exemplars presented in this article.

Ethnic differences in depression and anxiety among adults with atopic eczema: Population-based matched cohort studies within UK primary care.

BACKGROUND: Evidence demonstrates that individuals with atopic eczema (eczema) have increased depression and anxiety; however, the role of ethnicity in these associations is poorly understood. We aimed to investigate whether associations between eczema and depression or anxiety differed between adults from white and minority ethnic groups in the UK. METHODS: We used UK Clinical Practice Research Datalink GOLD to conduct matched cohort studies of adults (≥18 years) with ethnicity recorded in primary care electronic health records (April 2006-January 2020). We matched (age, sex, practice) adults with eczema to up to five adults without. We used stratified Cox regression with an interaction between eczema and ethnicity, to estimate hazard ratios (HRs) for associations between eczema and incident depression and anxiety in individuals from white ethnic groups and a pooled minority ethnic group (adults from Black, South Asian, Mixed and Other groups). RESULTS: We identified separate cohorts for depression (215,073 with eczema matched to 646,539 without) and anxiety (242,598 with eczema matched to 774,113 without). After adjusting for matching variables and potential confounders (age, sex, practice, deprivation, calendar period), we found strong evidence (p < 0.01) of ethnic differences in associations between eczema and depression (minority ethnic groups: HR = 1.33, 95% CI = 1.22,1.45; white ethnic groups: HR = 1.15, 95% CI = 1.12,1.17) and anxiety (minority ethnic groups: HR = 1.41, 95% CI = 1.28,1.55; white ethnic groups: HR = 1.17, 95% CI = 1.14,1.19). CONCLUSIONS: Adults with eczema from minority ethnic groups appear to be at increased depression and anxiety risk compared with their white counterparts. Culturally adapted mental health promotion and prevention strategies should be considered in individuals with eczema from minority ethnic groups.

Group B streptococcus infection during pregnancy and infancy: estimates of regional and global burden.

BACKGROUND: Group B streptococcus (GBS) colonisation during pregnancy can lead to invasive GBS disease (iGBS) in infants, including meningitis or sepsis, with a high mortality risk. Other outcomes include stillbirths, maternal infections, and prematurity. There are data gaps, notably regarding neurodevelopmental impairment (NDI), especially after iGBS sepsis, which have limited previous global estimates. In this study, we aimed to address this gap using newly available multicountry datasets. METHODS: We collated and meta-analysed summary data, primarily identified in a series of systematic reviews published in 2017 but also from recent studies on NDI and stillbirths, using Bayesian hierarchical models, and estimated the burden for 183 countries in 2020 regarding: maternal GBS colonisation, iGBS cases and deaths in infants younger than 3 months, children surviving iGBS affected by NDI, and maternal iGBS cases. We analysed the proportion of stillbirths with GBS and applied this to the UN-estimated stillbirth risk per country. Excess preterm births associated with maternal GBS colonisation were calculated using meta-analysis and national preterm birth rates. FINDINGS: Data from the seven systematic reviews, published in 2017, that informed the previous burden estimation (a total of 515 data points) were combined with new data (17 data points) from large multicountry studies on neurodevelopmental impairment (two studies) and stillbirths (one study). A posterior median of 19·7 million (95% posterior interval 17·9-21·9) pregnant women were estimated to have rectovaginal colonisation with GBS in 2020. 231 800 (114 100-455 000) early-onset and 162 200 (70 200-394 400) late-onset infant iGBS cases were estimated to have occurred. In an analysis assuming a higher case fatality rate in the absence of a skilled birth attendant, 91 900 (44 800-187 800) iGBS infant deaths were estimated; in an analysis without this assumption, 58 300 (26 500-125 800) infant deaths from iGBS were estimated. 37 100 children who recovered from iGBS (14 600-96 200) were predicted to develop moderate or severe NDI. 40 500 (21 500-66 200) maternal iGBS cases and 46 200 (20 300-111 300) GBS stillbirths were predicted in 2020. GBS colonisation was also estimated to be potentially associated with considerable numbers of preterm births. INTERPRETATION: Our analysis provides a comprehensive assessment of the pregnancy-related GBS burden. The Bayesian approach enabled coherent propagation of uncertainty, which is considerable, notably regarding GBS-associated preterm births. Our findings on both the acute and long-term consequences of iGBS have public health implications for understanding the value of investment in maternal GBS immunisation and other preventive strategies. FUNDING: Bill & Melinda Gates Foundation.

Plasma proteome analysis in HTLV-1-associated myelopathy/tropical spastic paraparesis.

BACKGROUND: Human T lymphotropic virus Type 1 (HTLV-1) causes a chronic inflammatory disease of the central nervous system known as HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM) which resembles chronic spinal forms of multiple sclerosis (MS). The pathogenesis of HAM remains uncertain. To aid in the differential diagnosis of HAM and to identify pathogenetic mechanisms, we analysed the plasma proteome in asymptomatic HTLV-1 carriers (ACs), patients with HAM, uninfected controls, and patients with MS. We used surface-enhanced laser desorption-ionization (SELDI) mass spectrometry to analyse the plasma proteome in 68 HTLV-1-infected individuals (in two non-overlapping sets, each comprising 17 patients with HAM and 17 ACs), 16 uninfected controls, and 11 patients with secondary progressive MS. Candidate biomarkers were identified by tandem Q-TOF mass spectrometry. RESULTS: The concentrations of three plasma proteins--high [ß2-microglobulin], high [Calgranulin B], and low [apolipoprotein A2]--were specifically associated with HAM, independently of proviral load. The plasma [ß2-microglobulin] was positively correlated with disease severity. CONCLUSIONS: The results indicate that monocytes are activated by contact with activated endothelium in HAM. Using ß2-microglobulin and Calgranulin B alone we derive a diagnostic algorithm that correctly classified the disease status (presence or absence of HAM) in 81% of HTLV-1-infected subjects in the cohort.

GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI.

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

The South Asian genome.

The genetic sequence variation of people from the Indian subcontinent who comprise one-quarter of the world's population, is not well described. We carried out whole genome sequencing of 168 South Asians, along with whole-exome sequencing of 147 South Asians to provide deeper characterisation of coding regions. We identify 12,962,155 autosomal sequence variants, including 2,946,861 new SNPs and 312,738 novel indels. This catalogue of SNPs and indels amongst South Asians provides the first comprehensive map of genetic variation in this major human population, and reveals evidence for selective pressures on genes involved in skin biology, metabolism, infection and immunity. Our results will accelerate the search for the genetic variants underlying susceptibility to disorders such as type-2 diabetes and cardiovascular disease which are highly prevalent amongst South Asians.

Balancing the robustness and predictive performance of biomarkers.

Recent studies have highlighted the importance of assessing the robustness of putative biomarkers identified from experimental data. This has given rise to the concept of stable biomarkers, which are ones that are consistently identified regardless of small perturbations to the data. Since stability is not by itself a useful objective, we present a number of strategies that combine assessments of stability and predictive performance in order to identify biomarkers that are both robust and diagnostically useful. Moreover, by wrapping these strategies around logistic regression classifiers regularized by the elastic net penalty, we are able to assess the effects of correlations between biomarkers upon their perceived stability. We use a synthetic example to illustrate the properties of our proposed strategies. In this example, we find that: (i) assessments of stability can help to reduce the number of false-positive biomarkers, although potentially at the cost of missing some true positives; (ii) combining assessments of stability with assessments of predictive performance can improve the true positive rate; and (iii) correlations between biomarkers can have adverse effects on their stability and hence must be carefully taken into account when undertaking biomarker discovery. We then apply our strategies in a proteomics context to identify a number of robust candidate biomarkers for the human disease HTLV1-associated myelopathy/tropical spastic paraparesis (HAM/TSP).