November 01, 2020

Ki Community Update: Vol. 3, Issue 2 – November 2020

In this issue of the Ki Community newsletter, we are excited to announce the launch of our website refresh. On our new site, you will find new information and features:

  • New content for external audiences – We are taking this opportunity to reach out to those beyond the foundation and our partners. Our new website has a focus on translating our findings to a wider audience.
  • Points of View – Point of view articles share perspectives from our team on data, data-driven decision-making, and accessibility.
  • Case Studies – Our new case studies tell the story of data science rallies with a narrative approach.
  • Resources – We will continue to provide resources and tools to support modeling and data visualization.

We plan to continue to update the site on regular basis to keep you informed.

Explore the new website at

We are also pleased to announce that the Institute for Disease Modeling (IDM) has recently joined the Bill and Melinda Gates Foundation. IDM is an internal data partner, focused on models to predict the spread of infectious diseases in populations. We welcome this new partner, the expanding use of data within the foundation, and new opportunities for collaboration.

At Ki, we continue to use quantitative analysis to support and shape the landscape of data with our grantees and partners and within BMGF. We look forward to continuing to build inter-institutional working groups and consortiums, increase interdisciplinary thought, and encourage a level of playing field of data and knowledge for all of our partners.

Finally, in this issue, we are focusing on the contributions Ki is making to better understand gestational age. We are presenting a new Ki Insight Story on gestational age data science rallies and an update on Ki Grand Challenges projects on the topic from grantees in India, Africa and Brazil.

Ki Insight Story – Gestational Age

Ki Insight Stories are an opportunity for us to thank the Ki Community for your generous data contributions and keep you informed on the important insights that Ki and our BMGF partners are generating.

Our latest Insight Story presents the results of the Gestational Age data science rallies. The data for these rallies represented over 2,500 individuals from at least 15 countries. This data made it possible to improve estimates of gestational age, which help determine whether a baby is small for its age or born preterm and, if so, intervene at that time.

The goals of these data science rallies were to improve estimates of gestational age at individual and population levels using ultrasound and non-ultrasound measurements.  When access to ultrasound early in pregnancy is unavailable, using ultrasound later in pregnancy or simpler metrics at birth has the potential to improve screening.

We deeply appreciate the hard work and involvement of all our data contributors, domain experts, and partners. Without you, this work would not be possible. We would particularly like to acknowledge the data contributors for the Gestational Age data science rallies and other collaborators:

Kelli K. Ryckman Samuel Newton Said Moh’d Ali
Jennifer B. Griffin Lisa Hurt Alexander Manu
Abdullah Baqui Fyezah Jehan Katherine EA Semrau
Parvez Ahmed Imran Nisar Davidson H. Hamer
Sushil Kanta Dasgupta Atiya Hussain Marina Straszak–Suri
Nazma Begum Naila Nadeem Caroline Grogan
Mahmoodur Rahman Muhammad Ilyas Godfrey Bemba
Nasreen Islam Anita Zaidi Anne CC Lee
Mohammad Quaiyum Sunil Sazawal Blair J Wylie
Betty Kirkwood Saikat Deb Alexander Manu
Karen Edmond Arup Dutta Sachiyo Yoshida
Caitlin Shannon Usha Dhingra Rajiv Bahl
Stephen Kennedy and the members of the International Fetal and Newborn Growth Consortium for the 21st Century

If you are a Ki data contributor or partner, we invite you to download the full Insight story by joining the Ki Insight Stories space on Synapse, a collaborative research platform.


  1. Log in or register for a Synpase account:
  2. Access the Ki Insight Stories folder, located within the KiData_PlatformServices folder:!Synapse:syn21237104Please note that if you are a first-time user of Synapse you will be asked to register and complete a quick certification quiz before your access is complete. This is because Synapse can contain sensitive information, so certification is required.

Grand Challenges Projects on Gestational Age

To continue our theme for this issue, we are presenting updates on Ki Grand Challenges projects that are focused on gestational age. Among the 31 data science-driven projects focusing on maternal, newborn and child health funded via the Ki Grand Challenges program in Africa, India, and Brazil, 5 projects are focused on gestational age.

Evaluating gestation age cutoff for defining premature births in Africa: Mortality prediction-based empiric approach using data science computational models approach.
PI: Said Mohammed Ali, Tanzania

This project is based on the use of a longitudinal cohort of 5000 pregnancies with ultrasound confirmed gestational age from the AMANHI study. The team is working on developing an artificial neural network (ANN) based model that will be able to improve prediction of gestational age.

Novel approaches for modelling Gestational Weight Gain trajectories using the Super Imposition Translation and Rotation growth model for predicting neonatal outcomes.
PI: Dr. Lucas Malla, Kenya

This project is based on the use of African studies available in the Ki data repository.

Size matters: Predicting personalized risk of SGA   
PIs:  Gautam Menon, Leelavati Narlikar, Uma Ram, P. Saravanan, Rahul Siddharthan

The project, which will be starting soon, aims to solve the problem of a large number of low-weight births in the Indian population and the inability to predict the risk reliably in the antenatal period.

New recommendations for gestational weight gain for SUS (Unified Health System)
PI: Gilberto Kac

Based on records for millions of women in Brazil, Gilberto Kac and his group developed a gestational weight gain curve that is more appropriate for the Brazilian population. These curves will be incorporated in materials used nationwide to monitor gestational weight.

Potential pregnancy days lost (PPDL): an innovative gestational age measure to assess maternal and child health interventions and outcomes
PI: Simone Diniz

This project aimed at increasing the granularity of gestational age (GA) data to days of pregnancy by exploring an innovative measure of gestational age, called “potential pregnancy days lost” (PPDL), to produce evidence of its association with maternal and child health, morbidity, and mortality in the short, medium, and long term.

Update on Data Sets

Thanks to you, Ki’s repository is ever-expanding. Please find below a list of studies that have completed QC after the last announcement.

  • FU-GWG – Brazil
    Determinant factors of insufficient and excessive gestational weight gain and maternal-child adverse outcomes
  • FU-LeptinGWG – Brazil
    To evaluate the effect of leptin and other selected variables on gestational weight gain (GWG) according to pre-gestational body mass index (BMI).
  • HU-NeoSurvival – Ethiopia
    Pregnancy rate and outcomes in rural eastern Ethiopia- Fetal and Neonatal survival
  • IRD-RECIPAL – Benin
    REtard de Croissance Intra-uterin et PALudisme (RECIPAL) is an original preconceptional cohort designed to assess the consequences of malaria during the first trimester of pregnancy, which is a poorly investigated period in Africa and during which malaria may be detrimental to the fetus.
  • JHU-MothersGift – Nepal
    Designs of two randomized, community-based trials to assess the impact of influenza immunization during pregnancy on espiratory illness among pregnant women and their infants and reproductive outcomes in rural Nepal
  • LSHTM-TIPs – India
    Trials of Improved Practices (TIPs) to Enhance the Dietary and Iron-Folate Intake during Pregnancy- A Quasi Experimental Study among Rural Pregnant Women of Varanasi, India
  • MISAME-1 – Burkina Faso
    Effects of maternal multiple micronutrient supplementation on fetal growth: a double-blind randomized controlled trial in rural Burkina Faso
  • MISAME-2 – Burkina Faso
    Effect of Prenatal Nutritional Supplementation on Birth Outcome in Hounde District, Burkina Faso
  • Mama-SASHA – Kenya
    Cohort study of the impact of an integrated agriculture, nutrition and health intervention on the Vitamin A and health status of mothers and their infants from pregnancy through 9 months postpartum
  • NWU-PMPEN – Kenya
    Pith Moromo 2: Cohort to Study Health Consequences of Food and Nutrition Insecurity During Pregnancy and Lactation
  • NWU-PreNAPS – Uganda
    To determine the differential impacts of food insecurity on gest. weight gain and prenatal depression, and b) to elucidate the mechanisms underlying the relationship between food insecurity and weight gain and/depression among HIV infected and HIV uninfected pregnant women in Gulu, Northern Uganda.
  • SBUMS-GDM – Iran
    Cost effectiveness of different screening strategies for gestational diabetes mellitus screening
  • SBUMS-Thyroid – Iran
    Thyroid and Pregnancy in Tehran, Iran: Objectives and Study Protocol
  • SBUMS-VitD – Iran
    Effectiveness of Prenatal Vitamin D Deficiency Screening and Treatment Program: A Stratified Randomized Field Trial
  • SPAZ-IPTp – Papua New Guinea
    Intermittent preventive treatment with azithromycin-containing regimens for the prevention of malarial infections and anemia and the control of sexually transmitted infections in pregnant women in Papua New Guinea
  • UC-RDNS – Bangladesh
    Rang Din Nutrition Study: An effectiveness study in Bangladesh evaluating the impact of lipid-based nutrient supplements (LNS) on preventing stunting and enhancing motor and cognitive development in young children, and improving the health and nutritional status of pregnant and lactating women.
  • UCL-NutriNepal – Nepal
    Effects of antenatal multiple micronutrient supplementation on birthweight and gestational duration in Nepal: double-blind, randomised controlled trial
  • UHAS-AHPI – Ghana
    Adiposity and hyperleptinemia during the first trimester among pregnant women with preeclampsia
  • UZ-MatNutri – Zimbabwe
    Effect of multimicronutrient supplementation on gestational length and birth size: a randomized, placebo-controlled, double-blind effectiveness trial in Zimbabwe

If you would like to learn more about these datasets or any other datasets in the repository, use our study explorer tool. The study explorer tool allows you to see all the data in the repository, at a metadata level, including the data you contributed. Here is the link: