Multi‐Treatment Inventory Allocation in Humanitarian Health Settings under Funding Constraints

There are 26 million refugees worldwide seeking safety from persecution, violence, conflict, and human rights violations. Camp‐based refugees are those that seek shelter in refugee camps, whereas urban refugees inhabit nearby, surrounding populations. The systems that supply aid to refugee camps may suffer from ineffective distribution due to challenges in administration, demand uncertainty and volatility in funding. Aid allocation should be carried out in a manner that properly balances the need of ensuring sufficient aid for camp‐based refugees, with the ability to share excess inventory, when available, with urban refugees that at times seek nearby camp‐based aid. We develop an inventory management policy to govern a camp's sharing of aid with urban refugee populations in the midst of uncertainties related to camp‐based and urban demands, and replenishment cycles due to funding issues. We use the policy to construct costs associated with: (i) referring urban populations elsewhere, (ii) depriving camp‐based refugee populations, and (iii) holding excess inventory in the refugee camp system. We then seek to allocate aid in a manner that minimizes the expected overall cost to the system. We propose two approaches to solve the resulting optimization problem, and conduct computational experiments on a real‐world case study as well as on synthetic data. Our results are complemented by an extensive simulation study that reveals broad support for our optimal thresholds and allocations to generalize across varied key parameters and distributions. We conclude by presenting related discussions that reveal key managerial insights into humanitarian aid allocation under uncertainty.

Section: Literature Reviewmentioning

confidence: 92%

Aid Allocation for Camp‐Based and Urban Refugees with Uncertain Demand and Replenishments

Azizi

Bozkir

Trapp

et al. 2021

“…They do not consider supply uncertainty and treatment interruption induced by stockouts; the main driver of treatment interruption and drug resistance in their model is reduced adherence of patients to follow-up visits if they live farther from the clinic. Another recent paper that is related to ours is Natarajan and Swaminathan (2017), which analyzes the problem of allocating limited inventory of drugs among patients with different health states. The main point of distinction here is that we model a chronic disease where the patients who receive treatment continue to return in subsequent periods and thus compete for drugs along with new patients whereas Natarajan and Swaminathan (2017) model an acute condition where the patients in the less severe health state are cured after treatment and exit the system.…”

Section: Global Health Supply Chainsmentioning

confidence: 99%

“…Another recent paper that is related to ours is Natarajan and Swaminathan (2017), which analyzes the problem of allocating limited inventory of drugs among patients with different health states. The main point of distinction here is that we model a chronic disease where the patients who receive treatment continue to return in subsequent periods and thus compete for drugs along with new patients whereas Natarajan and Swaminathan (2017) model an acute condition where the patients in the less severe health state are cured after treatment and exit the system. This leads to significant differences in the state transition equations and consequently the structure of the optimal policy.…”

Section: Global Health Supply Chainsmentioning

confidence: 99%

“…This leads to significant differences in the state transition equations and consequently the structure of the optimal policy. A secondary point of distinction is that Natarajan and Swaminathan (2017) focus on the impact of the timing and the variability in donor funding that is used to procure the drugs whereas our focus is on designing clinic-level program scale-up policies in the presence of an uncertain exogenous supply of drugs.…”

Section: Global Health Supply Chainsmentioning

confidence: 99%

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Optimal Scale‐Up of HIV Treatment Programs in Resource‐Limited Settings Under Supply Uncertainty

Deo

Mehta

Corbett

2022

This study is motivated by the challenges faced by clinics in sub‐Saharan Africa in allocating scarce and unreliable supply of antiretroviral drugs (ARVs) among a large pool of eligible patients. Existing discussion of ARV allocation is focused on qualitative rules for prioritizing certain socioeconomic and demographic patient segments over others at the national level. However, such prioritization rules are of limited utility in providing quantitative guidance on scaling up of treatment programs at individual clinics. In this study, we take the perspective of a clinic administrator whose objective is to maximize the quality‐adjusted survival of the entire patient population in its service area by allocating scarce and unreliable supply of drugs among two activities: initiating treatment for untreated patients and continuing treatment for previously treated patients. The key trade‐off underlying this allocation decision is between the marginal health benefit obtained by initiating an untreated patient on treatment and that obtained by avoiding treatment interruption of a treated patient. This trade‐off has not been explicitly studied in the clinical literature, which focuses either on the incremental value obtained from initiating treatment (over no treatment) or on the value of providing continuous treatment (over interrupted treatment) but not on the difference of the two. We cast the clinic's problem as a stochastic dynamic program and provide a partial characterization of the optimal policy, which consists of dynamic prioritization of patient segments and is characterized by state‐dependent thresholds. We use this structure of the optimal policy to design a simpler Two‐Period heuristic and show that it substantially outperforms the Safety‐Stock heuristic, which is commonly used in practice. In our numerical experiments based on realistic parameter values, the performance of the Two‐Period heuristic is within 4% of the optimal policy whereas that of the Safety‐Stock heuristic can be as much as 20% lower than that of the optimal policy. Our model can serve as a basis for developing a decision support tool for clinics to design their ARV treatment program scale‐up plans.

“…2016), funding systems (Gallien et al. 2017, Taylor and Xiao 2014), inventory allocation (Natarajan and Swaminathan 2017), distribution (Parvin et al. 2018) and procurement (Martin et al.…”

Section: Literaturementioning

confidence: 99%

Site Visit Frequency Policies for Mobile Family Planning Services

Vries

Swinkels

Wassenhove

2021

Improving access to family planning services is key to achieving many of the United Nations sustainable development goals. To scale up access in remote areas and urban slums, many developing countries deploy mobile family planning teams that visit “outreach sites” several times per year. Visit frequencies have a significant effect on the total number of clients served and hence the impact of the outreach program. Using a large dataset of visits in Madagascar, Uganda, and Zimbabwe, our study models the relationship between the number of clients seen during a visit and the time since the last visit and uses this model to analyze the characteristics of optimal frequencies. We use the latter to develop simple frequency policies for practical use, prove bounds on the worst‐case optimality gap, and test the impact of the policies with a simulation model. Our main finding is that despite the complexity of the frequency optimization problem, simple policies yield near‐optimal results. This holds even when few data are available and when the relationship between client volume and the time since the last visit is misspecified or substantially biased. The simulation for Uganda shows a potential increase in client numbers of between 7% and 10%, which corresponds to more than 12,000 additional families to whom family planning services could be provided. Our results can assist policymakers in determining when to start data‐driven frequency determination and which policies to implement.