A multi-method, multi-domain approach to evaluating preterm's effect on child growth and development

NIH RePORTER · NIH · R15 · $373,358 · view on reporter.nih.gov ↗

Abstract

Project Summary Preterm birth is a well-known determinant of poor child growth and development (CGAD). Premature infants have a higher risk of infection, malnutrition, and developmental impairments. Additionally, caregivers of preterm infants are at risk of developing mental health challenges, including depression, anxiety, and stress and in low- and middle-income countries, where 81% of preterm births occurs, caregivers frequently experience stigmatization leading to a loss of social support. These experiences have been shown to negatively impact CGAD by altering bonding and caregiver responsiveness. Although research shows that these factors influence CGAD through many pathways at many levels, each factor has traditionally been studied independently, therefore, these pathways are inadequately understood. This project aims to provide a clearer picture of these mechanisms and solutions to them through three main aims. The first is to identify child, family, and social factors that mediate and modify the effect of prematurity on CGAD. Caregivers of preterm infants will be recruited from well-child clinics at hospitals in Ghana, a country with high rate of preterm birth and developmental disabilities. Factors such as infection, malnutrition, feeding practices, parenting, maternal health, social stigma, and demographic characteristics will be measured using routinely collected maternal and child health data and questionnaires completed by caregivers. This data will be analyzed using path analysis, a statistical modeling technique that identifies causal pathways among many variables, in order to determine how these factors, interact and influence each other to determine CGAD. The second aim is to identify profiles of preterm and term babies who are at risk for poor growth and development. Machine learning algorithms will be applied to the maternal and child health data to identify the strongest predictors of growth and development. These predictors can be used to develop clinical screening tools to identify highly at-risk infants. The third aim is to identify local, caregiver-driven strategies that promote growth and development in preterm infants. This is based on Positive Deviant Theory, which posits that even in difficult circumstances, some individuals have uncommon but successful solutions. Caregivers whose preterm infants had high child development scores will be recruited to participate in qualitative interviews to learn about the strategies they use to achieve these positive outcomes. These results can be used to create an intervention for families of preterm infants to improve child growth and development. A multidisciplinary team of researchers will supervise and mentor graduate and undergraduate students from fields of computer science, health sciences, and child development. In addition to global research experience, these students will gain leadership, teamwork, and problem-solving skills that are invaluable to building future successful...

Key facts

NIH application ID
10795689
Project number
1R15HD114083-01
Recipient
OAKLAND UNIVERSITY
Principal Investigator
Kwame Sarfo Sakyi
Activity code
R15
Funding institute
NIH
Fiscal year
2024
Award amount
$373,358
Award type
1
Project period
2024-05-22 → 2027-04-30