JMIR Publications

ABSTRACT

Background:

Death in people living with HIV (PLWH) in sub-Saharan Africa (SSA) is highly preventable. However, the lack of inclusive and cost-effective high-performing prognostic tool constitutes a challenge. Most of the prognostic tools were developed in rich economies. The distinctive cultural dynamics in the epidemiology of HIV-related death makes them unsuitable for the region. In addition, in all the models, there was a lack of systematic stratification of the determinants of deaths based on clinical relevance and some included factors considered expensive for PLWH in SSA.

Objective:

To create a tailored predictive model that considers the unique context of sub-Saharan Africa, including cultural dynamics, cost-effectiveness, and clinical relevance.

Methods:

This a two-phase study. In the development phase, we will employ a combination of evidence synthesis namely meta-analysis, application epidemiology, biostatistical and economic paradigms to develop a prognostic model for PLWH in SSA. The Preferred Reporting Item for Systematic Reviews and Meta-Analyses Protocol will be followed in the structuring of the meta-analysis. From their creation to the present, we will search African journals (SABINET), PubMed, Scopus, Medline, Academic Search Complete, Directory of Open Access Repository, Cochrane Library, Web of Science, EMBASE, and the Cumulative Index for Nursing and Allied Health Literature. Only cohort studies with moderate to high quality will be included. The primary outcome variables include the predictors of HIV-related death and their corresponding effect sizes (RR), homogeneity of the predictors vis-a-vis SSA, cost implication, risk weight, clinical minimum important difference (CMID) and critical risk points. A random-effect meta-analysis model will be used to synthesize the unbiased estimate of risk namely the relative risk (RR) per predictor. A combination of risk weight and CMID will be used for risk stratification. The model's constituent items will be selected based on the combination of risk weight, CMID, and cost implications. We apply the emergent model to secondary data obtained from a cohort of PLWH in Eastern and Western Africa during the validation phase, with outcomes including sensitivity, specificity, calibration, discrimination and area under the curve.

Results:

The result will be presented using narrative and quantitative synthesis. Further, strength of association, temporality, consistency, biological gradient and specificity of the exposure-outcome association will be presented in table. The predictive algorithms will be presented in table.

Conclusions:

Effective prognostication coupled with intense monitoring and evaluation, and prioritizing of therapeutic targets possess the potential to positively turn around the fate of millions of PLWH at risk death in SSA.