Leveraging community health workers and a responsive digital health system to improve rates and timeliness of child well visits in the first year of life

University Of South Carolina At Columbia, Columbia SC

Investigators

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Abstract

ABSTRACT Timely well visits are essential to ensure the wellbeing of children in their first year of life as they provide opportunities for delivery of preventive health interventions as well as growth monitoring. Prior studies show high rates of gaps and delays in the delivery of preventive health interventions, indicative of gaps in well visits, and reflective of broader health system and community-level barriers. For example, in Tanzania, only 68% of children receive key recommended preventive health interventions within their first year of life. Utilizing community-based health workforce capacity and leveraging rapidly evolving digital health innovations hold great potential to mitigate gaps in service delivery, healthcare access, and caregiver knowledge in this context. Our multidisciplinary study team recently completed a project that demonstrated the feasibility and efficacy of mobile phone-based reminders and conditional financial incentives for improving rates and timeliness of routine preventive health interventions recommended for children. Our team has also shown that using community health workers (CHWs) is an acceptable approach for addressing caregivers’ knowledge gaps. Building on this prior work, we propose to develop and evaluate a machine learning approach that allows for the proactive identification of children who are most likely to experience missed or delayed child well visits. Early identification could allow for these children to be supported by our integrated, community-based, digital intervention to improve uptake and timeliness of recommended child well visits. This intervention, called “Huduma Kwa Wakati”, facilitates timely well visits through a CHW-led outreach and educational intervention that is supported by a combination of low-cost digital strategies (autonomous mobile phone-based child health protocols, reminders, clinic operations notifications; conditional incentive offers for adherence to visit schedules). To optimize the implementation and efficiency of Huduma Kwa Wakati, we propose the following specific aims: In Aim 1, we will initially collect retrospective data on the characteristics and child well visits of 800 children in their first year of life. Mothers with children ages 12-23 months will be enrolled from 40 geographic clusters in the catchment areas of 40 health facilities in four rural districts in Mwanza and Shinyanga regions of Tanzania. In Year 4, 800 pregnant women in their last trimester of pregnancy will be enrolled into a parallel two-arm clusterrandomized trial. Women will be enrolled from 80 geographic clusters in the catchment areas of the same 40 health facilities. 40 clusters will be randomized into the intervention arm; 40 clusters will be randomized into the control arm. Women will be followed for up to 15 months, until the child reaches 1 year of age. Child well visit dates will be documented using government-issued clinic cards. Concurrently, data on all child well visits in each catchment area since 2019 will be extracted from facility logbooks. In Aim 2, we will characterize the implementation context for our integrated, community-based intervention to improve rates and timeliness of child well visits, assess factors associated with its acceptability among mothers and key stakeholders, develop a training and implementation blueprint, and characterize the cost of the intervention. In Aim 3, we will evaluate the feasibility and potential efficacy of a machine learning approach for proactively identifying children who are most likely to experience missed or delayed child well visits and validate predictive models using data gathered in aim 1. Child well visits extracted from facility logbooks will serve as training data. Well visit data collected from the 800 study children will serve as validation data. Study findings will inform models and interventions to identify children who are most likely to experience missed or delayed well visits and the relevance and implementation of Huduma Kwa Wakati across a wide range of rural and low-resource community settings.