Prevalence and risk factors of Severe Acute Respiratory Syndrome Coronavirus 2 infection in women and children in peri-urban communities in Pakistan: A prospective cohort study

Background

Population-based seroepidemiological surveys provide accurate estimates of disease burden. We compare the COVID-19 prevalence estimates from two serial serological surveys and the associated risk factors among women and children in a peri-urban area of Karachi, Pakistan.

Methods

The AMANHI-COVID-19 study enrolled women and children between November 2020 and March 2021. Blood samples were collected from March to June 2021 (baseline) and September to December 2021 (follow-up) to test for anti-SARS-CoV-2 antibodies using ROCHE Elecsys®. Participants were visited or called weekly during the study for recording symptoms of COVID-19. We report the proportion of participants with anti-SARS-CoV-2 antibodies and symptoms in each survey and describe infection risk factors using step-wise binomial regression analysis.

Results

The adjusted seroprevalence among women was 45.3% (95% confidence interval (CI) = 42.6-47.9) and 82.3% (95% CI = 79.9-84.4) at baseline and follow-up survey, respectively. Among children, it was 18.4% (95% CI = 16.1-20.7) and 57.4% (95% CI = 54.3-60.3) at baseline and follow-up, respectively. Of the women who were previously seronegative, 404 (74.4%) tested positive at the follow-up survey, as did 365 (50.4%) previously seronegative children. There was a high proportion of asymptomatic infection. At baseline, being poorest and lacking access to safe drinking water lowered the risk of infection for both women (risk ratio (RR) = 0.8, 95% CI = 0.7-0.9 and RR = 1.2, 95% CI = 1.1-1.4, respectively) and children (RR = 0.7, 95% CI = 0.5-1.0 and RR = 1.4, 95% CI = 1.0-1.8, respectively). At the follow-up survey, the risk of infection was lower for underweight women and children (RR = 0.4, 95% CI = 0.3-0.7 and RR = 0.7, 95% CI = 0.5-0.8, respectively) and for women in the 30-39 years age group and children who were 24-36 months of age (RR = 0.6, 95% CI = 0.4-0.9 and RR = 0.7, 95% CI = 0.5-0.9, respectively). In both surveys, paternal employment was an important predictor of seropositivity among children (RR = 0.7, 95% CI = 0.6-0.9 and RR = 0.8, 95% CI = 0.7-1.0, respectively).

Conclusion

There was a high rate of seroconversion among women and children. Infection was generally mild. Parental education plays an important role in protection of children from COVID-19.

A, Barbano A, Barwick SW, Bastian B, Basu V, Baur S, Bay R, Beatty JJ, Becker KH, Becker Tjus J, Bellenghi C, BenZvi S, Berley D, Bernardini E, Besson DZ, Binder G, Bindig D, Blaufuss E, Blot S, Boddenberg M, Bontempo F, Borowka J, Böser S, Botner O, Böttcher J, Bourbeau E, Bradascio F, Braun J, Brinson B, Bron S, Brostean-Kaiser J, Browne S, Burgman A, Burley RT, Busse RS, Campana MA, Carnie-Bronca EG, Chen C, Chen Z, Chirkin D, Choi K, Clark BA, Clark K, Classen L, Coleman A, Collin GH, Conrad JM, Coppin P, Correa P, Cowen DF, Cross R, Dappen C, Dave P, De Clercq C, DeLaunay JJ, Delgado López D, Dembinski H, Deoskar K, Desai A, Desiati P, de Vries KD, de Wasseige G, de With M, DeYoung T, Diaz A, Díaz-Vélez JC, Dittmer M, Dujmovic H, Dunkman M, DuVernois MA, Dvorak E, Ehrhardt T, Eller P, Engel R, Erpenbeck H, Evans J, Evenson PA, Fan KL, Fazely AR, Fedynitch A, Feigl N, Fiedlschuster S, Fienberg AT, Filimonov K, Finley C, Fischer L, Fox D, Franckowiak A, Friedman E, Fritz A, Fürst P, Gaisser TK, Gallagher J, Ganster E, Garcia A, Garrappa S, Gerhardt L, Ghadimi A, Glaser C, Glauch T, Glüsenkamp T, Goldschmidt A, Gonzalez JG, Goswami S, Grant D, Grégoire T, Griswold S, Günther C, Gutjahr P, Haack C, Hallgren A, Halliday R, Halve L, Halzen F, Ha Minh M, Hanson K, Hardin J, Harnisch AA, Haungs A, Hebecker D, Helbing K, Henningsen F, Hettinger EC, Hickford S, Hignight J, Hill C, Hill GC, Hoffman KD, Hoffmann R, Hokanson-Fasig B, Hoshina K, Huang F, Huber M, Huber T, Hultqvist K, Hünnefeld M, Hussain R, Hymon K, In S, Iovine N, Ishihara A, Jansson M, Japaridze GS, Jeong M, Jin M, Jones BJP, Kang D, Kang W, Kang X, Kappes A, Kappesser D, Kardum L, Karg T, Karl M, Karle A, Katz U, Kauer M, Kellermann M, Kelley JL, Kheirandish A, Kin K, Kintscher T, Kiryluk J, Klein SR, Koirala R, Kolanoski H, Kontrimas T, Köpke L, Kopper C, Kopper S, Koskinen DJ, Koundal P, Kovacevich M, Kowalski M, Kozynets T, Kun E, Kurahashi N, Lad N, Lagunas Gualda C, Lanfranchi JL, Larson MJ, Lauber F, Lazar JP, Lee JW, Leonard K, Leszczyńska A, Li Y, Lincetto M, Liu QR, Liubarska M, Lohfink E, Lozano Mariscal CJ, Lu L, Lucarelli F, Ludwig A, Luszczak W, Lyu Y, Ma WY, Madsen J, Mahn KBM, Makino Y, Mancina S, Mariş IC, Martinez-Soler I, Maruyama R, Mase K, McElroy T, McNally F, Mead JV, Meagher K, Mechbal S, Medina A, Meier M, Meighen-Berger S, Micallef J, Mockler D, Montaruli T, Moore RW, Morse R, Moulai M, Naab R, Nagai R, Nahnhauer R, Naumann U, Necker J, Nguyen LV, Niederhausen H, Nisa MU, Nowicki SC, Nygren D, Obertacke Pollmann A, Oehler M, Oeyen B, Olivas A, O’Sullivan E, Pandya H, Pankova DV, Park N, Parker GK, Paudel EN, Paul L, Pérez de los Heros C, Peters L, Peterson J, Philippen S, Pieper S, Pittermann M, Pizzuto A, Plum M, Popovych Y, Porcelli A, Prado Rodriguez M, Price PB, Pries B, Przybylski GT, Raab C, Rack-Helleis J, Raissi A, Rameez M, Rawlins K, Rea IC, Rehman A, Reichherzer P, Reimann R, Renzi G, Resconi E, Reusch S, Rhode W, Richman M, Riedel B, Roberts EJ, Robertson S, Roellinghoff G, Rongen M, Rott C, Ruhe T, Ryckbosch D, Rysewyk Cantu D, Safa I, Saffer J, Sanchez Herrera SE, Sandrock A, Sandroos J, Santander M, Sarkar S, Sarkar S, Satalecka K, Schaufel M, Schieler H, Schindler S, Schmidt T, Schneider A, Schneider J, Schröder FG, Schumacher L, Schwefer G, Sclafani S, Seckel D, Seunarine S, Sharma A, Shefali S, Silva M, Skrzypek B, Smithers B, Snihur R, Soedingrekso J, Soldin D, Spannfellner C, Spiczak GM, Spiering C, Stachurska J, Stamatikos M, Stanev T, Stein R, Stettner J, Steuer A, Stezelberger T, Stokstad R, Stürwald T, Stuttard T, Sullivan GW, Taboada I, Ter-Antonyan S, Tilav S, Tischbein F, Tollefson K, Tönnis C, Toscano S, Tosi D, Trettin A, Tselengidou M, Tung CF, Turcati A, Turcotte R, Turley CF, Twagirayezu JP, Ty B, Unland Elorrieta MA, Valtonen-Mattila N, Vandenbroucke J, van Eijndhoven N, Vannerom D, van Santen J, Verpoest S, Walck C, Watson TB, Weaver C, Weigel P, Weindl A, Weiss MJ, Weldert J, Wendt C, Werthebach J, Weyrauch M, Whitehorn N, Wiebusch CH, Williams DR, Wolf M, Woschnagg K, Wrede G, Wulff J, Xu XW, Yanez JP, Yoshida S, Yu S, Yuan T, Zhang Z, Zhelnin P. Evidence for neutrino emission from the nearby active galaxy NGC 1068

A supermassive black hole, obscured by cosmic dust, powers the nearby active galaxy NGC 1068. Neutrinos, which rarely interact with matter, could provide information on the galaxy’s active core. We searched for neutrino emission from astrophysical objects using data recorded with the IceCube neutrino detector between 2011 and 2020. The positions of 110 known gamma-ray sources were individually searched for neutrino detections above atmospheric and cosmic backgrounds. We found that NGC 1068 has an excess of 79 − 20 + 22 neutrinos at tera–electron volt energies, with a global significance of 4.2σ, which we interpret as associated with the active galaxy. The flux of high-energy neutrinos that we measured from NGC 1068 is more than an order of magnitude higher than the upper limit on emissions of tera–electron volt gamma rays from this source.

Search for Unstable Sterile Neutrinos with the IceCube Neutrino Observatory

We present a search for an unstable sterile neutrino by looking for a resonant signal in eight years of atmospheric ν_{μ} data collected from 2011 to 2019 at the IceCube Neutrino Observatory. Both the (stable) three-neutrino and the 3+1 sterile neutrino models are disfavored relative to the unstable sterile neutrino model, though with p values of 2.8% and 0.81%, respectively, we do not observe evidence for 3+1 neutrinos with neutrino decay. The best-fit parameters for the sterile neutrino with decay model from this study are Δm_{41}^{2}=6.7_{-2.5}^{+3.9}  eV^{2}, sin^{2}2θ_{24}=0.33_{-0.17}^{+0.20}, and g^{2}=2.5π±1.5π, where g is the decay-mediating coupling. The preferred regions of the 3+1+decay model from short-baseline oscillation searches are excluded at 90% C.L.

Global newborn health research priorities identified in 2014: A review to evaluate the uptake

BACKGROUND

In 2014, World Health Organization published global research priorities for newborn health till 2025. We conducted this review to summarize completed or ongoing research on the twenty priorities.

METHODS

We conducted searches for twenty questions on MEDLINE via PubMed, Cochrane CENTRAL, Web of Science, clinical trial registries, and funder websites between July 2014 and May 2022. Studies addressing research questions using adequate design were included. Adequacy of uptake of a priority was assessed based on predefined criteria.

FINDINGS

The uptake of research priorities was high for 8 (40%), moderate for 11 (55%), and one priority, effectiveness of training community health workers (CHWs) to treat neonatal sepsis at home remains unaddressed. Priorities with moderate uptake include effectiveness of simplified neonatal resuscitation programme, simple clinical algorithms for CHWs to neonatal infection, CHWs training in basic neonatal resuscitation, community-initiated kangaroo mother care, perinatal audits, and novel tocolytic agents, scaling-up chlorhexidine cord application, stable surfactant with simpler administration, accurate, affordable methods to diagnose fetal distress, strategies for prevention and treatment of intrauterine growth retardation, and causal pathways for antenatal stillbirths.

INTERPRETATION

Adequate research was undertaken on pressing global concerns in newborn health. Funders and researchers should reflect on and address less researched areas.

FUNDING

None.

Sero-prevalence and risk factors for Severe Acute Respiratory Syndrome Coronavirus 2 infection in women and children in a rural district of Bangladesh: A cohort study

Background

Bangladesh reported its first COVID-19 case on March 8, 2020. Despite lockdowns and promoting behavioural interventions, as of December 31, 2021, Bangladesh reported 1.5 million confirmed cases and 27 904 COVID-19-related deaths. To understand the course of the pandemic and identify risk factors for SARs-Cov-2 infection, we conducted a cohort study from November 2020 to December 2021 in rural Bangladesh.

Methods

After obtaining informed consent and collecting baseline data on COVID-19 knowledge, comorbidities, socioeconomic status, and lifestyle, we collected data on COVID-like illness and care-seeking weekly for 54 weeks for women (n = 2683) and their children (n = 2433). Between March and July 2021, we tested all participants for SARS-CoV-2 antibodies using ROCHE's Elecsys® test kit. We calculated seropositivity rates and 95% confidence intervals (95% CI) separately for women and children. In addition, we calculated unadjusted and adjusted relative risk (RR) and 95% CI of seropositivity for different age and risk groups using log-binomial regression models.

Results

Overall, about one-third of women (35.8%, 95% CI = 33.7-37.9) and one-fifth of children (21.3%, 95% CI = 19.2-23.6) were seropositive for SARS-CoV-2 antibodies. The seroprevalence rate doubled for women and tripled for children between March 2021 and July 2021. Compared to women and children with the highest household wealth (HHW) tertile, both women and children from poorer households had a lower risk of infection (RR, 95% CI for lowest HHW tertile women (0.83 (0.71-0.97)) and children (0.75 (0.57-0.98)). Most infections were asymptomatic or mild. In addition, the risk of infection among women was higher if she reported chewing tobacco (RR = 1.19,95% CI = 1.03-1.38) and if her husband had an occupation requiring him to work indoors (RR = 1.16,  95% CI = 1.02-1.32). The risk of infection was higher among children if paternal education was >5 years (RR = 1.37, 95% CI = 1.10-1.71) than in children with a paternal education of ≤5 years.

Conclusions

We provided prospectively collected population-based data, which could contribute to designing feasible strategies against COVID-19 tailored to high-risk groups. The most feasible strategy may be promoting preventive care practices; however, collecting data on reported practices is inadequate. More in-depth understanding of the factors related to adoption and adherence to the practices is essential.

Strong Constraints on Neutrino Nonstandard Interactions from TeV-Scale ν_{μ} Disappearance at IceCube

We report a search for nonstandard neutrino interactions (NSI) using eight years of TeV-scale atmospheric muon neutrino data from the IceCube Neutrino Observatory. By reconstructing incident energies and zenith angles for atmospheric neutrino events, this analysis presents unified confidence intervals for the NSI parameter ε_{μτ}. The best-fit value is consistent with no NSI at a p value of 25.2%. With a 90% confidence interval of -0.0041≤ε_{μτ}≤0.0031 along the real axis and similar strength in the complex plane, this result is the strongest constraint on any NSI parameter from any oscillation channel to date.

Preoperative evaluation of pleural adhesions with dynamic chest radiography: a retrospective study of 146 patients with lung cancer

AIM

To assess the utility of dynamic chest radiography (DCR) during the preoperative evaluation of pleural adhesions.

MATERIALS AND METHODS

Sequential chest radiographs of 146 patients with lung cancer were acquired during forced respiration using a DCR system. The presence of pleural adhesions and their grades were determined by retrospective surgery video assessment (absent: 121, present: 25). The maximum inspiration to expiration lung area ratio was used as an index for air intake volume. A ratio of ≥0.65 was regarded as insufficient respiration. Two radiologists assessed the images for pleural adhesions based on motion findings. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were compared for each adhesion grade and patient group (patients with sufficient/insufficient respiration). Pearson's chi-squared test compared the group. Statistical significance was set at p<0.05.

RESULTS

DCR correctly identified 22/25 patients with pleural adhesions, with 20 false-positive results (sensitivity, 88%; specificity, 83.5%; PPV, 52.4%; NPV, 97.12%). Although the diagnostic performances for the various adhesion grades were similar, specificity in patients with sufficient respiration increased to 93.9% (31/33), identifying all cases except for those with loose adhesions.

CONCLUSIONS

DCR images revealed restricted and/or distorted motions in lung structures and structural tension in patients with pleural adhesions. DCR could be a useful technique for routine preoperative evaluation of pleural adhesions. Further development of computerised methods can assist in the quantitative assessment of abnormal motion findings.

Prediction of gestational age using urinary metabolites in term and preterm pregnancies

Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC–MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.

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.

Research priorities to reduce the impact of COVID-19 in low- and middle-income countries

Background

The COVID-19 pandemic has caused disruptions to the functioning of societies and their health systems. Prior to the pandemic, health systems in low- and middle-income countries (LMIC) were particularly stretched and vulnerable. The International Society of Global Health (ISoGH) sought to systematically identify priorities for health research that would have the potential to reduce the impact of the COVID-19 pandemic in LMICs.

Methods

The Child Health and Nutrition Research Initiative (CHNRI) method was used to identify COVID-19-related research priorities. All ISoGH members were invited to participate. Seventy-nine experts in clinical, translational, and population research contributed 192 research questions for consideration. Fifty-two experts then scored those questions based on five pre-defined criteria that were selected for this exercise: 1) feasibility and answerability; 2) potential for burden reduction; 3) potential for a paradigm shift; 4) potential for translation and implementation; and 5) impact on equity.

Results

Among the top 10 research priorities, research questions related to vaccination were prominent: health care system access barriers to equitable uptake of COVID-19 vaccination (ranked 1st), determinants of vaccine hesitancy (4th), development and evaluation of effective interventions to decrease vaccine hesitancy (5th), and vaccination impacts on vulnerable population/s (6th). Health care delivery questions also ranked highly, including: effective strategies to manage COVID-19 globally and in LMICs (2nd) and integrating health care for COVID-19 with other essential health services in LMICs (3rd). Additionally, the assessment of COVID-19 patients’ needs in rural areas of LMICs was ranked 7th, and studying the leading socioeconomic determinants and consequences of the COVID-19 pandemic in LMICs using multi-faceted approaches was ranked 8th. The remaining questions in the top 10 were: clarifying paediatric case-fatality rates (CFR) in LMICs and identifying effective strategies for community engagement against COVID-19 in different LMIC contexts.

Interpretation

Health policy and systems research to inform COVID-19 vaccine uptake and equitable access to care are urgently needed, especially for rural, vulnerable, and/or marginalised populations. This research should occur in parallel with studies that will identify approaches to minimise vaccine hesitancy and effectively integrate care for COVID-19 with other essential health services in LMICs. ISoGH calls on the funders of health research in LMICs to consider the urgency and priority of this research during the COVID-19 pandemic and support studies that could make a positive difference for the populations of LMICs.

Burden and risk factors for antenatal depression and its effect on preterm birth in South Asia: A population-based cohort study

Introduction

Women experience high rates of depression, particularly during pregnancy and the postpartum periods. Using population-based data from Bangladesh and Pakistan, we estimated the burden of antenatal depression, its risk factors, and its effect on preterm birth.

Methods

The study uses the following data: maternal depression measured between 24 and 28 weeks of gestation using the 9–question Patient Health Questionnaire (PHQ-9); data on pregnancy including an ultrasound before 19 weeks of gestation; data on pregnancy outcomes; and data on woman’s age, education, parity, weight, height, history of previous illness, prior miscarriage, stillbirth, husband’s education, and household socioeconomic data collected during early pregnancy. Using PHQ-9 cutoff score of ≥12, women were categorized into none to mild depression or moderate to moderately severe depression. Using ultrasound data, preterm birth was defined as babies born <37 weeks of gestation. To identify risk ratios (RR) for antenatal depression, unadjusted and adjusted RR and 95% confidence intervals (CI) were calculated using log- binomial model. Log-binomial models were also used for determining the effect of antenatal depression on preterm birth adjusting for potential confounders. Data were analyzed using Stata version 16 (StataCorp LP).

Results

About 6% of the women reported moderate to moderately severe depressive symptoms during the antenatal period. A parity of ≥2 and the highest household wealth status were associated with an increased risk of depression. The overall incidence of preterm birth was 13.4%. Maternal antenatal depression was significantly associated with the risk of preterm birth (ARR, 95% CI: 1.34, 1.02–1.74).

Conclusion

The increased risk of preterm birth in women with antenatal depression in conjunction with other significant risk factors suggests that depression likely occurs within a constellation of other risk factors. Thus, to effectively address the burden of preterm birth, programs require developing and providing integrated care addressing multiple risk factors.

Search for Relativistic Magnetic Monopoles with Eight Years of IceCube Data

We present an all-sky 90% confidence level upper limit on the cosmic flux of relativistic magnetic monopoles using 2886 days of IceCube data. The analysis was optimized for monopole speeds between 0.750c and 0.995c, without any explicit restriction on the monopole mass. We constrain the flux of relativistic cosmic magnetic monopoles to a level below 2.0×10^{-19}  cm^{-2} s^{-1} sr^{-1} over the majority of the targeted speed range. This result constitutes the most strict upper limit to date for magnetic monopoles with β≳0.8 and up to β∼0.995 and fills the gap between existing limits on the cosmic flux of nonrelativistic and ultrarelativistic magnetic monopoles.

Performance of a validated spontaneous preterm delivery predictor in South Asian and Sub-Saharan African women: a nested case control study

OBJECTIVES

To address the disproportionate burden of preterm birth (PTB) in low- and middle-income countries, this study aimed to (1) verify the performance of the United States-validated spontaneous PTB (sPTB) predictor, comprised of the IBP4/SHBG protein ratio, in subjects from Bangladesh, Pakistan and Tanzania enrolled in the Alliance for Maternal and Newborn Health Improvement (AMANHI) biorepository study, and (2) discover biomarkers that improve performance of IBP4/SHBG in the AMANHI cohort.

STUDY DESIGN

The performance of the IBP4/SHBG biomarker was first evaluated in a nested case control validation study, then utilized in a follow-on discovery study performed on the same samples. Levels of serum proteins were measured by targeted mass spectrometry. Differences between the AMANHI and U.S. cohorts were adjusted using body mass index (BMI) and gestational age (GA) at blood draw as covariates. Prediction of sPTB < 37 weeks and < 34 weeks was assessed by area under the receiver operator curve (AUC). In the discovery phase, an artificial intelligence method selected additional protein biomarkers complementary to IBP4/SHBG in the AMANHI cohort.

RESULTS

The IBP4/SHBG biomarker significantly predicted sPTB < 37 weeks (n = 88 vs. 171 terms ≥ 37 weeks) after adjusting for BMI and GA at blood draw (AUC= 0.64, 95% CI: 0.57-0.71, p < .001). Performance was similar for sPTB < 34 weeks (n = 17 vs. 184 ≥ 34 weeks): AUC = 0.66, 95% CI: 0.51-0.82, p = .012. The discovery phase of the study showed that the addition of endoglin, prolactin, and tetranectin to the above model resulted in the prediction of sPTB < 37 with an AUC= 0.72 (95% CI: 0.66-0.79, p-value < .001) and prediction of sPTB < 34 with an AUC of 0.78 (95% CI: 0.67-0.90, p < .001).

CONCLUSION

A protein biomarker pair developed in the U.S. may have broader application in diverse non-U.S. populations.

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.

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.