Claire Adida and Jennifer Burney of the University of California, San Diego, in the U.S. are developing a mobile money platform to enable the rural poor in West Africa to pay school fees directly and securely using mobile phones to help more children stay in school. Mobile money is not widely used in West Africa and many individuals do not use banks. Often money for things like school fees has to be physically transferred over long distances, which is unreliable and takes time. Recording payments is done largely by hand, which is error-prone and difficult to track. In Phase I, they designed the mobile money platform in conjunction with their mobile provider partner for use on feature phones, which are common in the region. They also designed a school payment database for recording transactions and issuing receipts, and an SMS-based messaging platform for schools to notify people and raise additional funds. They pilot-tested their approach in a secondary school in Benin with over 600 students, and recorded substantial interest in making mobile money payments, with around 3% of fees paid using their platform. In Phase II, they will work to overcome the challenges in Phase I, which included difficulties integrating their platform into the existing mobile provider’s system, and refine the payment platform and database. They will then implement the platform in all public schools in Benin that teach children between 11 and around 18 years old. They will perform a randomized control trial to evaluate how well their platform is adopted, and its effect on the general uptake of mobile money.

William Fifer of Columbia University in the U.S. is developing a non-invasive method to measure heart rate and heart rate variability in the fetus during pregnancy as a window into brain function to help warn of emerging brain abnormalities. They aim to produce charts of brain development beginning during pregnancy and continuing into early childhood that can be used in limited-resource settings for monitoring child health. In Phase I, they analyzed heart rate data from fetuses obtained during different sleep states from 356 pregnant mothers in South Africa, and found that it correlated with their brain activity measured around four days after birth. In Phase II, they will extend the analysis to infants between 24 and 36 months to determine whether the heart rate patterns measured in utero and the brain activity measured soon after birth are also related to various aspects of neurodevelopment including growth, cognition, and language in toddlers. They will also study a new cohort of 50 pregnant mothers to optimize the heart rate and brain activity measurements to help identify the best markers of abnormal brain development that can be used in low-resource settings for timely interventions.

George Wehby and colleagues at the University of Iowa in the U.S. will evaluate newborn metabolic biomarkers for their ability to predict gestational age, and identify associations between them and long-term academic achievement. They will analyze existing newborn metabolic profiles and academic tests from almost one million children in Iowa born between 1980 and 2006 to identify the most predictive biomarkers. In the future they will expand their method to developing countries to help estimate gestational age and identify newborns at risk of neurodevelopmental defects. Being able to accurately determine gestational age is critical in preterm birth, which is the leading cause of child death worldwide. And knowing which regions have the highest incidence of preterm births would help better target prevention strategies. In Phase I, they identified candidate biomarkers that can be detected by tandem mass spectrometry using existing dried blood spot samples and based on around 150,000 children born between 1980 and 2006 in Iowa, they developed a predictive model for gestational age. In Phase II, through a grant awarded to Kelli Ryckman, their model will be tested using around 2,000 cord blood samples from newborns in Bangladesh, Pakistan and Tanzania, and refine it to improve accuracy by measuring additional biomarkers such as hemoglobin.

Jennifer Griffin of RTI International in the U.S. will test whether the gestational age of infants born prematurely can be determined by combining simple physical measurements with an automated analysis of the blood vessels in the retina using the camera on a mobile phone. As the fetus develops in the womb, blood vessels in the anterior lens gradually disappear, which closely correlates with gestational age at delivery. An automated approach increases accuracy and reproducibility, and their new method is also lower cost and requires less skill than traditional ultrasound-based methods of gestational age dating, and therefore should be useful also in low-middle income settings. In Phase I, they developed methods for acquiring quality images of the eye using an ophthalmoscope and smartphone. In Phase II, they will perform a multisite cross-sectional study to generate retinal images and biophysical parameter datasets from preterm neonates for developing the image analysis software and producing an algorithm to identify the best predictors of gestational age. They will evaluate their approach by comparing it to the gold standard of ultrasound.