Upcoming Seminar – Sydney & Yamkela LI post

📢 Upcoming Seminar

We are pleased to invite you to an engaging seminar featuring two complementary talks that explore how advanced statistical modelling can be used to better understand child health outcomes in sub-Saharan Africa. They examine the social, economic, and structural determinants of childhood disease and mortality, assess hidden household and community-level variations, and evaluate how methodological choices influence public health conclusions.

🗓 Date: 24 February 2026
⏰ Time: 14:00–15:00 (SAST)
🔗 Join the seminar: https://bit.ly/45ir990

🔹 Talk 1

Modelling childhood malaria in case study of Liberia (MIS 2022)

Presenter: Sydney Sambo

Key focus areas:

· Socioeconomic Determinants of Childhood Malaria

· Prevention and Intervention Effectiveness

· Policy and Public Health Impact

🔍Why It Matters:
Childhood malaria remains a leading cause of morbidity and mortality in many African countries. By identifying the socioeconomic and structural drivers of infection, this research supports more targeted, evidence-based interventions. The findings provide critical insights for policymakers and public health practitioners to optimize resource allocation, strengthen prevention strategies, and reduce health inequalities among vulnerable populations.

🔹 Talk 1

Modelling under-five child mortality in Tanzania including estimation of heterogeneity.

Presenter: Yamkela Mayise

Key Focus Areas:

· Identify key factors influencing under-five child mortality using regression models.

· Assess heterogeneity/frailty at household and community levels.

· Evaluate temporal changes in results using Complete Case Analysis (CCA) versus Multiple Imputation (MI).

· Compare datasets from 2015/16 and 2022 in Tanzania to identify similarities or differences.

🔍Why It Matters:
Under-five mortality is a key indicator of national health and development. Understanding both measurable risk factors and hidden heterogeneity at household and community levels allows for more precise intervention planning. Comparing different analytical approaches and time periods strengthens the reliability of findings and highlights evolving risk patterns, ultimately guiding more effective child survival policies and health system improvements.