Improving HIV Early Infant Diagnosis Supply Chains in Sub-Saharan Africa: Models and Application to Mozambique

Abstract

Early diagnosis of HIV among infants born to HIV infected mothers is critical because roughly 50% of untreated infected infants die before the age of two years. Yet most countries in sub-Saharan Africa experience significant delays in diagnosis due to operational inefficiencies in early infant diagnosis (EID) networks. We develop a two-part modeling framework relying on optimization and simulation to generate operational improvements in the assignment of clinics to laboratories and the allocation of capacity across laboratories, and evaluate the associated impact on the number of infants initiating treatment. Applying our methodology to EID program data from Mozambique, we validate our simulation model and estimate that optimally re-assigning clinics to labs would decrease the average sample turnaround time (TAT) by 11% and increase the number of infected infants starting treatment by about 4\% relative to the current system. Further, consolidating all diagnostic capacity in one centralized lab would decrease average TATs by an estimated 22% and increase the number of infected infants initiating treatment by 7%. Our sensitivity analysis suggests that the consolidation of capacity in a single location would remain near-optimal across a wide range of laboratory utilization levels in Mozambique. However, this full consolidation solution is dominated by configurations with two or more labs for EID networks with average transportation times larger than those currently observed in Mozambique by at least 15%.

Keywords: Facility location; queueing networks; supply chain optimization; HIV prevention; early infant diagnosis