Learning from health system actor and caregiver experiences in Ghana and Nepal to strengthen growth monitoring and promotion

by Akriti Singh, Kelsey A. Torres, Nashna Maharjan, Jyoti Shrestha, Faith Agbozo, Abdulai Abubakari, Lutuf Abdul-Rahman, Altrena Mukuria-Ashe

Globally, growth monitoring and promotion (GMP) of infants and young children is a fundamental component of routine preventive child health care; however, programs have experienced varying degrees of quality and success with enduring challenges. The objective of this study was to describe implementation of GMP (growth monitoring, growth promotion, data use, and implementation challenges) in two countries, Ghana and Nepal, to identify key actions to strengthen GMP programs.

We conducted semi-structured key informant interviews with national and sub-national government officials (n = 24), health workers and volunteers (n = 40), and caregivers (n = 34). We conducted direct structured observations at health facilities (n = 10) and outreach clinics (n = 10) to complement information from interviews. We coded and analyzed interview notes for themes related to GMP implementation.

Health workers in Ghana (e.g., community health nurses) and Nepal (e.g., auxiliary nurse midwives) had the knowledge and skills to assess and analyze growth based on weight measurement. However, health workers in Ghana centered growth promotion on the growth trend (weight-for-age over time), whereas health workers in Nepal based growth promotion on measurement from one point in time to determine whether a child was underweight. Overlapping challenges included health worker time and workload. Both countries tracked growth-monitoring data systematically; however, there was variation in growth monitoring data use.

This study shows that GMP programs may not always focus on the growth trend for early detection of growth faltering and preventive actions. Several factors contribute to this deviation from the intended goal of GMP. To overcome them, countries need to invest in both service delivery (e.g., decision-making algorithm) and demand generation efforts (e.g., integrate with responsive care and early learning).