Since 2017, the Thrasher Research Fund has supported a study to further an epidemiologic understanding of post-discharge mortality among young infants (0-6 months of age), as well as to develop clinical prediction models that can be used to identify those at-risk of post-discharge mortality.

This project has been conducted in parallel with a 7,000-person pragmatic effectiveness study of an innovative discharge program for children aged 6 months to 5 years, which uses their individualized risk score to guide delivery of the educational and community-referral program.

As of August 2019, we have successfully transitioned all sites from the control phase to the interventional phase, which will continue through the end of 2020. Proof-of-concept work has demonstrated a 3-fold increase in appropriate health-seeking behavior and a 2-fold increase in post-discharge hospital re-admissions, a critical outcome as two-thirds of post-discharge deaths occur outside of health facilities. We also showed a 30% reduction in mortality, although this was not statistically significant due to the small sample size.

Our current interventional study is showing a similar effectiveness of the intervention, though final results will not be available for between six to eight months.

Smart Discharges for Mom and Baby

In 2019, the CICH expanded the Smart Discharges program to include a cohort of 3,200 mother-newborn dyads presenting at Mbarara Regional Referral Centre in Uganda. This project aims to build a clinical risk prediction model that will identify mother-infant dyads at highest risk for death and complications after discharge from hospital. Prediction of risk based on the mother and infant as a pair is a major gap in current research and vital to infant survival.

The ultimate goal is to develop an evidence-based bundle of care for both the mother and neonate, in which low-risk mother-infant pairs receive less burdensome (yet pragmatic and feasible) postpartum care, while high risk pairs receive a more extensive bundle of interventions. In low-resource settings, this is imperative to maximize the efficiency of resource allocation and to improve health outcomes.