Developing and Validating an Inclusive and Cost-Effective Prediction Algorithm for Survival and Death Among People...

Area of Science:

• Epidemiology
• Biostatistics
• Health Economics

Background:

• Premature mortality in people with HIV in sub-Saharan Africa (SSA) is preventable but hindered by a lack of inclusive, cost-effective prognostic tools.
• Existing tools are often unsuitable for SSA due to distinct cultural dynamics and high costs of included factors.
• There's a need for models that systematically stratify death determinants by clinical relevance within the SSA context.

Purpose of the Study:

• To develop a tailored predictive model for HIV-related mortality in SSA.
• The model will incorporate cultural dynamics, cost-effectiveness, and clinical relevance specific to the region.
• To improve prognostication for people living with HIV (PLHIV) at risk of premature death in SSA.

Main Methods:

• A two-phase study involving evidence synthesis (meta-analysis) and epidemiological/biostatistical/economic paradigms.
• Systematic literature search following PRISMA protocol across multiple databases, including African journals.
• Development of a prognostic model using meta-analysis of cohort studies, calculating risk metrics (Rw, CMID, PID), and cost implications for risk stratification.

Main Results:

• The study is projected to run from October 2025 to September 2026, with results expected in November 2026.
• A narrative and quantitative synthesis will present indices of causality (strength of association, temporality, etc.).
• The area under the receiver operating characteristic curve (AUC) will determine the optimal critical risk point for the predictive algorithm.

Conclusions:

• Effective prognostication is key to improving outcomes for PLHIV at risk of premature death in SSA.
• Integrating monitoring, evaluation, and prioritized therapeutic targets can significantly impact mortality rates.
• A contextually relevant and cost-effective prognostic tool can empower interventions and save lives.