Optimal Decentralization of Early Infant Diagnosis of HIV in Resource-Limited Settings

Abstract

Early infant diagnosis (EID) programs in many resource-limited settings are aimed at diagnosing infants born to HIV positive mothers. Due to the complexity of the diagnostic technology, EID programs are often highly centralized with few laboratories testing blood samples from a large network of health facilities. This leads to long diagnostic delays and consequent failure of patients to collect results in a timely manner. Several point-of-care (POC) devices that provide rapid diagnosis within the health facilities are being developed to mitigate these drawbacks of centralized EID networks. We study the decision of which facilities should receive the POC device (the placement plan) using the EID program in Mozambique as a case-study. We argue that the choice of an appropriate plan is critical to maximizing the public health impact of POC devices in the presence of tight budget constraints. To formalize this argument, we develop a detailed simulation model to evaluate the impact of a placement plan. It comprises two parts: an operational model that quantifies the impact of a POC placement plan on the diagnostic delay and a behavioral part that quantifies the impact of diagnostic delay on the likelihood of result collection by infants' caregivers. We also develop an approximate version of these operational and patient behavior dynamics and embed them in an optimization model to generate candidate POC placement plans. We find that the optimization based plan can result in up to 30% more patients collecting their results compared to rules of thumb that have practical appeal. Finally, we show that the effectiveness of POC devices is much higher than other operational improvements to the EID network such as increased laboratory capacity, reduced transportation delay, and more regularized transport.

Inventory Management in Humanitarian Operations: Impact of Amount, Schedule, and Uncertainty in Funding

“…Within humanitarian operations, a majority of the papers focus on disaster relief; e.g., Beamon and Kotleba (2006) and Duran et al (2011) focus on inventory management during emergencies. In recent years, long-term public health issues have also received attention from the operations management community; e.g., Rashkova et al (2011), Deo and Sohoni (2014), and Taylor and Xiao (2014). Our work is more closely related to Rashkova et al (2011).…”

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

“…From an operational perspective two opposing trends are currently observed for most HIV testing results on the optimal control of batch service queues (Deb and Serfozo 1973). At the network level, it would be interesting to combine the decision models from this paper with that of Deo and Sohoni (2015) to jointly optimize the existing laboratory network and POC device placement. We believe that the quantitative framework presented here may constitute a useful tool when considering these research questions.…”

“…The work of Deo and Sohoni (2015), which also lies at the intersection of the above three streams of literature, is closest to our paper in its approach. It also develops optimization and simulation models for operational decisions in EID networks but differs substantially in its objective, methodology, decisions considered, and complexity.…”

OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research

“…For example, the concept of "delayed differentiation" in the operations management literature (e.g., Tang 1997, Swaminathan andTayur 1998) entails creatively revamping the business model of a manufacturer or service provider such that it focuses on the generic parts and does not commit to custom options until the last stages. If we view healthcare delivery as a supply-chain design problem, society has a choice in terms of the timing of offering common services (preventive care, screening, and Alizamir et al (2013); Angalakudati et al (2014); Ata et al (2017); Bertsimas et al (2013); ; Deo and Sohoni (2015); Deo et al (2013); ; Huh et al (2013); Mamani et al (2013); Natarajan and Swaminathan (2014); Sandikçi et al (2013) MeT2: Design of Delivery Gawande (2010aGawande ( , 2011aGawande ( , 2012; Makary (2013); Weick and Sutcliffe (2015) Berwick et al (2006); Blumenthal (1996);Brook et al (1996); Donabedian (1988); Grol and Grimshaw (2003); Newman-Toker and Pronovost (2009) health-related activities) and custom services (diagnostic and treatment services) to the population. Inspired by the concept of "delayed differentiation," Thompson et al (2018) empirically demonstrate the improvement in the value of care through implementing "temporal displacement of care" with a panel of 45,000 patients in Vermont, with an objective of shifting costly diagnostic and treatment services offered to sick patients to more intensive preventive services offered to healthy individuals.…”

“…In elucidating the institutional context underlying HOM 2.0, we use the US healthcare system as the main backdrop to this paper, while noting that the literature has touched on many different parts of the world, including Germany (Kuntz et al 2014), Hong Kong (Guo et al 2018), India (Deo and Sohoni 2015), Singapore (Dai and Shi 2017), South Korea (Cho et al 2014), sub-Saharan Africa (Jónasson et al 2017), and the UK (Freeman et al 2017), to provide a few examples.…”