Development and validation of a simplified algorithm for neonatal gestational age assessment - protocol for the Alliance for Maternal Newborn Health Improvement (AMANHI) prospective cohort study

Assessing the diagnostic accuracy of postnatal clinical scoring methods and foot length measurement for estimating gestational age and birthweight of newborns in low- and middle-income countries: a systematic review and meta-analysis

BACKGROUND

This study aimed to update systematic reviews and meta-analyses on the diagnostic accuracy of postnatal clinical scoring (PCS) methods and foot length (FL) measurement for assessing gestational age (GA) and birth weight in low-income and middle-income countries (LMICs). In addition, the quality of reference standards, including antenatal ultrasound (A-US), last menstrual period (LMP), PCS and newborn weighing scales, was also evaluated.

METHODS

Studies from LMICs published between January 2000 and February 2024 were searched, using databases such as PubMed, Web of Science, Cochrane Library, CINAHL and Scopus. Studies that compared PCS and/or FL with LMP and/or A-US to estimate GA or used calibrated newborn weighing scales for birthweight estimation were included. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-II tool and evaluated the quality of the reference standards. When sufficient data were available, pooled estimates were calculated using random-effects models.

RESULTS

A total of 50 studies were included. A-US was a reasonable tool for GA assessment if conducted by physicians using fetal biometry and the Hadlock method for GA estimation. LMP was reasonable when women had regular cycles, knew their LMP, were not using contraceptives and LMP data were collected by healthcare providers. When A-US was used as the reference standard, PCS methods estimated GA with a precision of ±2.8 to ±3.2 weeks. FL measurement <7.5 cm showed a pooled sensitivity of 76.2% and specificity of 36.6% for identifying preterm birth. FL measurement ≤7.6 cm had a pooled sensitivity of 78.6% and specificity of 65.7% for identifying low birth weight (LBW). High heterogeneity across studies was observed.

CONCLUSION

This systematic review and meta-analysis highlights significant variability and methodological inconsistencies in using PCS methods and FL measurement for estimating GA and LBW in LMICs. The observed high heterogeneity across studies suggests a cautious interpretation of the results.

PROSPERO REGISTRATION NUMBER

CRD42020209455.

Perinatal inflammation, fetal growth restriction, and long-term neurodevelopmental impairment in Bangladesh

BACKGROUND

There are limited data on the impact of perinatal inflammation on child neurodevelopment in low-middle income countries and among growth-restricted infants.

METHODS

Population-based, prospective birth cohort study of 288 infants from July 2016-March 2017 in Sylhet, Bangladesh. Umbilical cord blood was analyzed for interleukin(IL)-1α, IL-1β, IL-6, IL-8, and C-reactive protein(CRP). Child neurodevelopment was assessed at 24 months with Bayley-III Scales of Infant Development. We determined associations between cord blood inflammation and neurodevelopmental outcomes, controlling for potential confounders.

RESULTS

248/288 (86%) live born infants were followed until 24 months, among whom 8.9% were preterm and 45.0% small-for-gestational-age(SGA) at birth. Among all infants, elevated concentrations (>75%) of CRP and IL-6 at birth were associated with increased odds of fine motor delay at 24 months; elevated CRP was also associated with lower receptive communication z-scores. Among SGA infants, elevated IL-1α was associated with cognitive delay, IL-8 with language delay, CRP with lower receptive communication z-scores, and IL-1β with lower expressive communication and motor z-scores.

CONCLUSIONS

In rural Bangladesh, perinatal inflammation was associated with impaired neurodevelopment at 24 months. The associations were strongest among SGA infants and noted across several biomarkers and domains, supporting the neurobiological role of inflammation in adverse fetal development, particularly in the setting of fetal growth restriction.

IMPACT

Cord blood inflammation was associated with fine motor and language delays at 24 months of age in a community-based cohort in rural Bangladesh. 23.4 million infants are born small-for-gestational-age (SGA) globally each year. Among SGA infants, the associations between cord blood inflammation and adverse outcomes were strong and consistent across several biomarkers and neurodevelopmental domains (cognitive, motor, language), supporting the neurobiological impact of inflammation prominent in growth-restricted infants. Prenatal interventions to prevent intrauterine growth restriction are needed in low- and middle-income countries and may also result in long-term benefits on child development.

Cohort profile: the Pregnancy Risk Infant Surveillance and Measurement Alliance (PRISMA) - Pakistan

PURPOSE

Pakistan has disproportionately high maternal and neonatal morbidity and mortality. There is a lack of detailed, population-representative data to provide evidence for risk factors, morbidities and mortality among pregnant women and their newborns. The Pregnancy Risk, Infant Surveillance and Measurement Alliance (PRISMA) is a multicountry open cohort that aims to collect high-dimensional, standardised data across five South Asian and African countries for estimating risk and developing innovative strategies to optimise pregnancy outcomes for mothers and their newborns. This study presents the baseline maternal and neonatal characteristics of the Pakistan site occurring prior to the launch of a multisite, harmonised protocol.

PARTICIPANTS

PRISMA Pakistan study is being conducted at two periurban field sites in Karachi, Pakistan. These sites have primary healthcare clinics where pregnant women and their newborns are followed during the antenatal, intrapartum and postnatal periods up to 1 year after delivery. All encounters are captured electronically through a custom-built Android application. A total of 3731 pregnant women with a mean age of 26.6±5.8 years at the time of pregnancy with neonatal outcomes between January 2021 and August 2022 serve as a baseline for the PRISMA Pakistan study.

FINDINGS TO DATE

In this cohort, live births accounted for the majority of pregnancy outcomes (92%, n=3478), followed by miscarriages/abortions (5.5%, n=205) and stillbirths (2.6%, n=98). Twenty-two per cent of women (n=786) delivered at home. One out of every four neonates was low birth weight (<2500 g), and one out of every five was preterm (gestational age <37 weeks). The maternal mortality rate was 172/100 000 pregnancies, the neonatal mortality rate was 52/1000 live births and the stillbirth rate was 27/1000 births. The three most common causes of neonatal deaths obtained through verbal autopsy were perinatal asphyxia (39.6%), preterm births (19.8%) and infections (12.6%).

FUTURE PLANS

The PRISMA cohort will provide data-driven insights to prioritise and design interventions to improve maternal and neonatal outcomes in low-resource regions.

TRIAL REGISTRATION NUMBER

NCT05904145.

The association between adverse pregnancy outcomes and non-viral genital pathogens among women living in sub-Saharan Africa: a systematic review

Adverse pregnancy outcomes are the main causes of maternal and neonatal morbidity and mortality, including long-term physical and psychological sequelae. These events are common in low- and middle-income countries, particularly in Sub Saharan Africa, despite national efforts. Maternal infections can cause complications at any stage of pregnancy and contribute to adverse outcomes. Among infections, those of the genital tract are a major public health concern worldwide, due to limited availability of prevention, diagnosis and treatment approaches. This applies even to treatable infections and holds true especially in Sub-Saharan Africa. As late as 2017, the region accounted for 40% of all reported treatable non-viral genital pathogens worldwide, many of which have been independently associated with various adverse pregnancy outcomes, and that include Chlamydia trachomatis, Neisseria gonorrhoeae, Trichomonas vaginalis, Treponema pallidum. Two databases (PubMed and Embase) were examined to identify eligible studies published up to October 2022. This study reviewed findings on the association between infections by treatable non-viral genital pathogens during pregnancy and adverse pregnancy outcomes among women living in Sub-Saharan Africa. Articles' title and abstract were screened at first using keywords as "sexually transmitted infections", "non-viral", "adverse pregnancy outcome", "Africa", "sub-Saharan Africa", "pregnant women", "pregnancy", and "pregnancy outcome". Subsequently, according to the eligibility criteria, potential articles were read in full. Results showed that higher risk of preterm birth is associated with Treponema pallidum, Chlamydia trachomatis and Candida albicans infections. Additionally, rates of stillbirth, neonatal death, low birth weight and intrauterine growth restriction are also associated with Treponema pallidum infection. A better insight on the burden of non-viral genital pathogens and their effect on pregnancy is needed to inform antenatal care guidelines and screening programs, to guide the development of innovative diagnostic tools and other strategies to minimize transmission, and to prevent short- and long-term complications for mothers and children.

Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation: Facilitating use of this technology in low resource settings of South Asia and East Africa

Background

Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches emerged in response to a global health need; because in most of the developing world, accurate antenatal gestational age estimates are not feasible. These methods initially developed in North America have now been externally validated in two studies in developing countries, however, require shipment of samples at sub-zero temperature.

Methods

A subset of 330 pairs of heel prick dried blood spot samples were shipped on dry ice and in ambient temperature from field sites in Tanzania, Bangladesh and Pakistan to laboratory in Iowa (USA). We evaluated impact on recovery of analytes of shipment temperature, developed and evaluated models for predicting gestational age using a limited set of metabolic screening analytes after excluding 17 analytes that were impacted by shipment conditions of a total of 44 analytes.

Results

With the machine learning model using all the analytes, samples shipped in dry ice yielded a Root Mean Square Error (RMSE) of 1.19 weeks compared to 1.58 weeks for samples shipped in ambient temperature. Out of the 44 screening analytes, recovery of 17 analytes was significantly different between the two shipment methods and these were excluded from further machine learning model development. The final model, restricted to stable analytes provided a RMSE of 1.24 (95% confidence interval (CI) = 1.10-1.37) weeks for samples shipped on dry ice and RMSE of 1.28 (95% CI = 1.15-1.39) for samples shipped at ambient temperature. Analysis for discriminating preterm births (gestational age <37 weeks), yielded an area under curve (AUC) of 0.76 (95% CI = 0.71-0.81) for samples shipped on dry ice and AUC of 0.73 (95% CI = 0.67-0.78) for samples shipped in ambient temperature.

Conclusions

In this study, we demonstrate that machine learning algorithms developed using a sub-set of newborn screening analytes which are not sensitive to shipment at ambient temperature, can accurately provide estimates of gestational age comparable to those from published regression models from North America using all analytes. If validated in larger samples especially with more newborns <34 weeks, this technology could substantially facilitate implementation in LMICs.

Population-based rates, risk factors and consequences of preterm births in South-Asia and sub-Saharan Africa: A multi-country prospective cohort study

BACKGROUND

Preterm birth is the leading cause of neonatal deaths in low middle-income countries (LMICs), yet there exists a paucity of high-quality data from these countries. Most modelling estimates are based on studies using inaccurate methods of gestational age assessment. We aimed to fill this gap by measuring the population-based burden of preterm birth using early ultrasound dating in five countries in South-Asian and sub-Saharan Africa.

METHODS

We identified women early in pregnancy (<20 weeks based on last menstrual period) by home visits every 2-3 months (except in Zambia where they were identified at antenatal care clinics) in 5 research sites in South-Asia and sub-Saharan Africa between July 2012 and September 2016. Trained sonographers performed an ultrasound scan for gestational age dating. Women were enrolled if they were 8-19 weeks pregnant on ultrasound. Women <8 weeks were rescheduled for repeat scans after 4 weeks, and identified women were followed through pregnancy until 6 weeks postpartum. Site-specific rates and proportions were calculated and a logistic regression model was used to predict the risk factors of preterm birth.

RESULTS

Preterm birth rates ranged from 3.2% in Ghana to 15.7% in Pakistan. About 46% of all neonatal deaths occurred among preterm infants, 49% in South Asia and 40% in sub-Saharan Africa. Fourteen percent of all preterm infants died during the neonatal period. The mortality was 37.6% for early preterm babies (<34 weeks), 5.9% for late preterm babies (34 to <37 weeks), and 1.7% for term babies (37 to <42 weeks). Factors associated lower gestation at birth included South-Asian region (adjusted mean difference (Adj MD) = -6.2 days, 95% confidence interval (CI) = -5.5, -6.9), maternal morbidities (Adj MD = -3.4 days, 95% CI = -4.6, -2.2), multiple pregnancies (Adj MD = -17.8 days, 95% CI = -19.9,-15.8), adolescent pregnancy (Adj MD = -2.7 days, 95% CI = -3.7, -1.6) and lowest wealth quintile (Adj MD = -1.3 days, 95% CI = -2.4, -0.3).

CONCLUSIONS

Preterm birth rates are higher in South Asia than in sub-Saharan Africa and contribute to 49% and 40% of all neonatal deaths in the two regions, respectively. Adolescent pregnancy and maternal morbidities are modifiable risk factors associated with preterm birth.

Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa

BACKGROUND

Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1-2 weeks of ultrasonography-based GA. We sought to leverage machine learning algorithms to improve accuracy and applicability of this approach to LMICs settings.

METHODS

This study uses data from AMANHI-ACT, a prospective pregnancy cohorts in Asia and Africa where early pregnancy ultrasonography estimated GA and birth weight are available and metabolite screening data in a subset of 1318 new-borns were also available. We utilized this opportunity to develop machine learning (ML) algorithms. Random Forest Regressor was used where data was randomly split into model-building and model-testing dataset. Mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate performance. Bootstrap procedures were used to estimate confidence intervals (CI) for RMSE and MAE. For pre-term birth identification ROC analysis with bootstrap and exact estimation of CI for area under curve (AUC) were performed.

RESULTS

Overall model estimated GA had MAE of 5.2 days (95% CI 4.6-6.8), which was similar to performance in SGA, MAE 5.3 days (95% CI 4.6-6.2). GA was correctly estimated to within 1 week for 85.21% (95% CI 72.31-94.65). For preterm birth classification, AUC in ROC analysis was 98.1% (95% CI 96.0-99.0; p < 0.001). This model performed better than Iowa regression, AUC Difference 14.4% (95% CI 5-23.7; p = 0.002).

CONCLUSIONS

Machine learning algorithms and models applied to metabolomic gestational age dating offer a ladder of opportunity for providing accurate population-level gestational age estimates in LMICs settings. These findings also point to an opportunity for investigation of region-specific models, more focused feasible analyte models, and broad untargeted metabolome investigation.

Simplified models to assess newborn gestational age in low-middle income countries: findings from a multicountry, prospective cohort study

INTRODUCTION

Preterm birth is the leading cause of child mortality. This study aimed to develop and validate programmatically feasible and accurate approaches to estimate newborn gestational age (GA) in low resource settings.

METHODS

The WHO Alliance for Maternal and Newborn Health Improvement (AMANHI) study recruited pregnant women from population-based cohorts in five countries (Bangladesh, Ghana, Pakistan, Tanzania and Zambia). Women <20 weeks gestation by ultrasound-based dating were enrolled. Research staff assessed newborns for: (1) anthropometry, (2) neuromuscular/physical signs and (3) feeding maturity. Machine-learning techniques were used to construct ensemble models. Diagnostic accuracy was assessed by areas under the receiver operating curve (AUC) and Bland-Altman analysis.

RESULTS

7428 liveborn infants were included (n=536 preterm, <37 weeks). The Ballard examination was biased compared with ultrasound dating (mean difference: +9 days) with 95% limits of agreement (LOA) -15.3 to 33.6 days (precision ±24.5 days). A model including 10 newborn characteristics (birth weight, head circumference, chest circumference, foot length, breast bud diameter, breast development, plantar creases, skin texture, ankle dorsiflexion and infant sex) estimated GA with no bias, 95% LOA ±17.3 days and an AUC=0.88 for classifying the preterm infant. A model that included last menstrual period (LMP) with the 10 characteristics had 95% LOA ±15.7 days and high diagnostic accuracy (AUC 0.91). An alternative simpler model including birth weight and LMP had 95% LOA of ±16.7 and an AUC of 0.88.

CONCLUSION

The best machine-learning model (10 neonatal characteristics and LMP) estimated GA within ±15.7 days of early ultrasound dating. Simpler models performed reasonably well with marginal increases in prediction error. These models hold promise for newborn GA estimation when ultrasound dating is unavailable.

Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation

BACKGROUND

Globally, 15 million infants are born preterm and another 23.2 million infants are born small for gestational age (SGA). Determining burden of preterm and SGA births, is essential for effective planning, modification of health policies and targeting interventions for reducing these outcomes for which accurate estimation of gestational age (GA) is crucial. Early pregnancy ultrasound measurements, last menstrual period and post-natal neonatal examinations have proven to be not feasible or inaccurate. Proposed algorithms for GA estimation in western populations, based on routine new-born screening, though promising, lack validation in developing country settings. We evaluated the hypothesis that models developed in USA, also predicted GA in cohorts of South Asia (575) and Sub-Saharan Africa (736) with same precision.

METHODS

Dried heel prick blood spots collected 24-72 hours after birth from 1311 new-borns, were analysed for standard metabolic screen. Regression algorithm based, GA estimates were computed from metabolic data and compared to first trimester ultrasound validated, GA estimates (gold standard).

RESULTS

Overall Algorithm (metabolites + birthweight) estimated GA to within an average deviation of 1.5 weeks. The estimated GA was within the gold standard estimate by 1 and 2 weeks for 70.5% and 90.1% new-borns respectively. Inclusion of birthweight in the metabolites model improved discriminatory ability of this method, and showed promise in identifying preterm births. Receiver operating characteristic (ROC) curve analysis estimated an area under curve of 0.86 (conservative bootstrap 95% confidence interval (CI) = 0.83 to 0.89); P < 0.001) and Youden Index of 0.58 (95% CI = 0.51 to 0.64) with a corresponding sensitivity of 80.7% and specificity of 77.6%.

CONCLUSION

Metabolic gestational age dating offers a novel means for accurate population-level gestational age estimates in LMIC settings and help preterm birth surveillance initiatives. Further research should focus on use of machine learning and newer analytic methods broader than conventional metabolic screen analytes, enabling incorporation of region-specific analytes and cord blood metabolic profiles models predicting gestational age accurately.

The Global Network Maternal Newborn Health Registry: a multi-country, community-based registry of pregnancy outcomes

BACKGROUND

The Global Network for Women's and Children's Health Research (Global Network) conducts clinical trials in resource-limited countries through partnerships among U.S. investigators, international investigators based in in low and middle-income countries (LMICs) and a central data coordinating center. The Global Network's objectives include evaluating low-cost, sustainable interventions to improve women's and children's health in LMICs. Accurate reporting of births, stillbirths, neonatal deaths, maternal mortality, and measures of obstetric and neonatal care is critical to determine strategies for improving pregnancy outcomes. In response to this need, the Global Network developed the Maternal Newborn Health Registry (MNHR), a prospective, population-based registry of pregnant women, fetuses and neonates receiving care in defined catchment areas at the Global Network sites. This publication describes the MNHR, including participating sites, data management and quality and changes over time.

METHODS

Pregnant women who reside in or receive healthcare in select communities are enrolled in the MNHR of the Global Network. For each woman and her offspring, sociodemographic, health care, and the major outcomes through 42-days post-delivery are recorded. Study visits occur at enrollment during pregnancy, at delivery and at 42 days postpartum.

RESULTS

From 2010 through 2018, the Global Network MNHR sites were located in Guatemala, Belagavi and Nagpur, India, Pakistan, Democratic Republic of Congo, Kenya, and Zambia. During this period at these sites, 579,140 pregnant women were consented and enrolled in the MNHR, nearly 99% of all eligible women. Delivery data were collected for 99% of enrolled women and 42-day follow-up data for 99% of those delivered. In this supplement, the trends over time and assessment of differences across geographic regions are analyzed in a series of 18 manuscripts utilizing the MNHR data.

CONCLUSIONS

Improving maternal, fetal and newborn health in countries with poor outcomes requires an understanding of the characteristics of the population, quality of health care and outcomes. Because the worst pregnancy outcomes typically occur in countries with limited health registration systems and vital records, alternative registration systems may prove to be highly valuable in providing data. The MNHR, an international, multicenter, population-based registry, assesses pregnancy outcomes over time in support of efforts to develop improved perinatal healthcare in resource-limited areas. Trial Registration The Maternal Newborn Health Registry is registered at Clinicaltrials.gov (ID# NCT01073475). Registered February 23, 2019. https://clinicaltrials.gov/ct2/show/NCT01073475.

Performance of late pregnancy biometry for gestational age dating in low-income and middle-income countries: a prospective, multicountry, population-based cohort study from the WHO Alliance for Maternal and Newborn Health Improvement (AMANHI) Study Group

BACKGROUND

We aimed to evaluate and improve the accuracy of the ultrasound scan in estimating gestational age in late pregnancy (ie, after 24 weeks' gestation) in low-income and middle-income countries (LMICs), where access to ultrasound in the first half of pregnancy is rare and where intrauterine growth restriction is prevalent.

METHODS

This prospective, population-based, cohort study was done in three LMICs (Bangladesh, Pakistan, and Tanzania) participating in the WHO Alliance for Maternal and Newborn Health Improvement study. Women carrying a live singleton fetus dated by crown-rump length (CRL) measurements between 8+0-14+6 weeks of gestation, who were willing to return for two additional ultrasound scans, and who planned on delivering in the study area were enrolled in the study. Participants underwent ultrasonography at 24+0-29+6 weeks and at 30+0-36+6 weeks' gestation. Birthweights were measured within 72 h of birth, and the proportions of infants who had a small-for-gestational-age birthweight (ie, a birthweight <10% of the standard birthweight for the infant's gestational age and sex according to the INTERGROWTH-21st project newborn baby reference standards) and appropriate-for-gestational-age birthweights were ascertained. Estimation of gestational age by standard fetal biometry measurements in addition to transcerebellar diameter (TCD) measurements was compared with gold-standard CRL measurements by use of Bland-Altman plots to calculate the mean difference and 95% limits of agreement. Statistical modelling was done to develop new gestational age prediction formulas for third trimester ultrasonography in LMICs.

FINDINGS

Between Feb 7, 2015, and Jan 9, 2017, 1947 women were enrolled in the study. 1387 pregnant women had an ultrasound scan at 24+0-29+6 weeks of gestation and 1403 had an ultrasound scan between 30+0-36+6 weeks of gestation. Of the 1379 unique infants whose birthweights were available, 981 (71·1%) infants were born with an appropriate-for-gestational-age birthweight and 398 (28·9%) infants were born with a small-for-gestational-age birthweight. The accuracy of late pregnancy ultrasound biometry using existing formulas to estimate gestational age in LMICs was similar to that in high-income settings. With standard dating formulas, late pregnancy ultrasound at 24+0-29+6 weeks' gestation was accurate to within approximately plus or minus 2 weeks of the gold-standard CRL measurement of gestational age, and late pregnancy ultrasound was accurate to within ±3 weeks of the CRL measurement at 30+0-36+6 weeks' gestation. In infants who were ultimately born small for gestational age, individual parameters systematically underestimated gestational age, apart from TCD, which showed minimal bias. By use of a novel parsimonious model formula that combined TCD with femur length, gestational age at the 24+0 -29+6-week ultrasound scan was estimated to within ±10·5 days of the CRL measurement and estimated to within ±15·1 days of the CRL measurement at the 30+0-36+6-week ultrasound scan. Similar results were observed in infants who were small-for-gestational-age.

INTERPRETATION

Incorporation of TCD and the use of new formulas in late pregnancy ultrasound scans could improve the accuracy of gestational age estimation in both appropriate-for-gestational-age and small-for-gestational-age infants in LMICs. Given the high rates of small-for-gestational-age infants in LMICs, these results might be especially relevant. Validation of this new formula in other LMIC populations is needed to establish whether the accuracy of the late pregnancy ultrasound can be narrowed to within approximately 2 weeks.

FUNDING

Bill & Melinda Gates Foundation.

A Pregnancy Cohort to Study Multidimensional Correlates of Preterm Birth in India: Study Design, Implementation, and Baseline Characteristics of the Participants

Globally, preterm birth is a major public health problem. In India, 3.6 million of the 27 million infants born annually are preterm. Risk stratification of women based on multidimensional risk factors assessed during pregnancy is critical for prevention of preterm birth. A cohort study of pregnant women was initiated in May 2015 at the civil hospital in Gurugram, Haryana, India. Women are enrolled within 20 weeks of gestation and are followed until delivery and once postpartum. The objectives are to identify clinical, epidemiologic, genomic, epigenomic, proteomic, and microbial correlates; discover molecular-risk markers by using an integrative -omics approach; and generate a risk-prediction algorithm for preterm birth. We describe here the longitudinal study design, methodology of data collection, and the repositories of data, biospecimens, and ultrasound images being created. A total of 4,326 pregnant women, with documented evidence of recruitment before 20 weeks of gestation, have been enrolled through March 2018. We report baseline characteristics and outcomes of the first 2,000 enrolled participants. A high frequency of preterm births (14.9% among 1,662 live births) is noteworthy. The cohort database and the repositories will become global resources to answer critical questions on preterm birth and other birth outcomes.

The assessment of gestational age: a comparison of different methods from a malaria pregnancy cohort in sub-Saharan Africa

BACKGROUND

Determining gestational age in resource-poor settings is challenging because of limited availability of ultrasound technology and late first presentation to antenatal clinic. Last menstrual period (LMP), symphysio-pubis fundal height (SFH) and Ballard Score (BS) at delivery are therefore often used. We assessed the accuracy of LMP, SFH, and BS to estimate gestational age at delivery and preterm birth compared to ultrasound (US) using a large dataset derived from a randomized controlled trial in pregnant malaria patients in four African countries.

METHODS

Mean and median gestational age for US, LMP, SFH and BS were calculated for the entire study population and stratified by country. Correlation coefficients were calculated using Pearson's rho, and Bland Altman plots were used to calculate mean differences in findings with 95% limit of agreements. Sensitivity, specificity, positive predictive value and negative predictive value were calculated considering US as reference method to identify term and preterm babies.

RESULTS

A total of 1630 women with P. falciparum infection and a gestational age > 24 weeks determined by ultrasound at enrolment were included in the analysis. The mean gestational age at delivery using US was 38.7 weeks (95%CI: 38.6-38.8), by LMP, 38.4 weeks (95%CI: 38.0-38.9), by SFH, 38.3 weeks (95%CI: 38.2-38.5), and by BS 38.0 weeks (95%CI: 37.9-38.1) (p < 0.001). Correlation between US and any of the other three methods was poor to moderate. Sensitivity and specificity to determine prematurity were 0.63 (95%CI 0.50-0.75) and 0.72 (95%CI, 0.66-0.76) for LMP, 0.80 (95%CI 0.74-0.85) and 0.74 (95%CI 0.72-0.76) for SFH and 0.42 (95%CI 0.35-0.49) and 0.77 (95%CI 0.74-0.79) for BS.

CONCLUSIONS

In settings with limited access to ultrasound, and in women who had been treated with P. falciparum malaria, SFH may be the most useful antenatal tool to date a pregnancy when women present first in second and third trimester. The Ballard postnatal maturation assessment has a limited role and lacks precision. Improving ultrasound facilities and skills, and early attendance, together with the development of new technologies such as automated image analysis and new postnatal methods to assess gestational age, are essential for the study and management of preterm birth in low-income settings.

Evolution of clinical method for new-born infant maturity assessment

In the routine practice of neonatology, differentiating preterm premature new-born from small-for-date (SFD) new-born infant is an essential aspect to anticipate different clinical scenarios and monitor accordingly. Clinical assessment of new-born maturity is an invincible tool in resource poor areas for the purpose, without any prior investment. Over the past decades, clinical method for new-born infant maturity assessment has evolved intricately. From defining prematures with a mere statement of birth weight to clinical assessment of new-born as per gestational age with a comprehensive scheme based on neural and physical maturity characteristics of a new-born, clinical method for new-born maturity assessment has evolved substantially to the present where we stand. A complete review on the evolutionary history of clinical method for new-born infant maturity assessment will enable researchers in this field to get acquainted with the trend of past research work in accordance to the recent advancement all over the world. In the process, the lacunae still present in this area of study can be spotted which will invite new research proposals. Looking into the recent context, clinical method for assessing new-born infant maturity is making further forward shift with an attempt to quantify neuromuscular maturity criteria with further precision and incorporation of additional criteria."What is known - What is New" (Authors' summary)What is knownNeuro-muscular and external physical characteristic assessment together has greater significance for evaluating new-born infant's maturity as per gestational age over using individual one of them.Evaluation of brain maturity through passive muscle tone assessment of new-born infants with different maneuvers has the imperative role in determining new-born infant maturity.What is newClinical method for determining new-born infant maturity as per gestational age is being made explicit with the incorporation of criteria like feeding behavior of the new-born and objective assessment of anthropometric parameters, beside neuro-muscular and external physical characteristics evaluation.Neuro-muscular maturity can be quantified further with absolute values or closer range of values of different maneuvers and signs used in the clinical method for evaluating new-born infant maturity as per gestational age with more precision.

Profile: Health and Demographic Surveillance System in peri-urban areas of Karachi, Pakistan

The Aga Khan University’s Health and Demographic Surveillance System (HDSS) in peri urban areas of Karachi was set up in the year 2003 in four low socioeconomic communities and covers an area of 17.6 square kilometres. Its main purpose has been to provide a platform for research projects with the focus on maternal and child health improvement, as well as educational opportunities for trainees. The total population currently under surveillance is 249,128, for which a record of births, deaths, pregnancies and migration events is maintained by two monthly household visits. Verbal autopsies for stillbirths, deaths of children under the age of five years and adult female deaths are conducted. For over a decade, the HDSS has been a platform for a variety of studies  including, calculation of the incidence of various infectious diseases like typhoid bacteremia, pneumonia and diarrhea, evaluation of effectiveness of various treatment regimens for neonatal sepsis, assessment of the acceptance of hospitalized care, determination of the etiology of moderate to severe diarrhea, assessment of burden and etiology of neonatal sepsis and a multi-centre cohort study measuring the burden of stillbirths, neonatal and maternal deaths. We have also established a bio-repository of a well-defined maternal and newborn cohort. Through a well-established HDSS rooted in maternal and child health we aim to provide concrete evidence base to guide policy makers to make informed decisions at local, national and international levels.