Impact of treatment heterogeneity on drug resistance and supply chain costs

Eirini Spiliotopoulou, Maciej F. Boni, Prashant Yadav

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The efficacy of scarce drugs for many infectious diseases is threatened by the emergence and spread of resistance. Multiple studies show that available drugs should be used in a socially optimal way to contain drug resistance. This paper studies the tradeoff between risk of drug resistance and operational costs when using multiple drugs for a specific disease. Using a model for disease transmission and resistance spread, we show that treatment with multiple drugs, on a population level, results in better resistance-related health outcomes, but more interestingly, the marginal benefit decreases as the number of drugs used increases. We compare this benefit with the corresponding change in procurement and safety stock holding costs that result from higher drug variety in the supply chain. Using a large-scale simulation based on malaria transmission dynamics, we show that disease prevalence seems to be a less important factor when deciding the optimal width of drug assortment, compared to the duration of one episode of the disease and the price of the drug(s) used. Our analysis shows that under a wide variety of scenarios for disease prevalence and drug cost, it is optimal to simultaneously deploy multiple drugs in the population. If the drug price is high, large volume purchasing discounts are available, and disease prevalence is high, it may be optimal to use only one drug. Our model lends insights to policy makers into the socially optimal size of drug assortment for a given context. © 2013 The Authors.
Original languageEnglish
Pages (from-to)158-171
JournalSocio-Economic Planning Sciences
Volume47
Issue number3
DOIs
Publication statusPublished - Sep 2013
Externally publishedYes

Fingerprint

drug resistance
Drug Resistance
Supply Chain
Drugs
drug
supply
Costs
costs
cost
disease prevalence
Disease
assortment
Supply chain
disease transmission
Malaria
disease resistance
Purchasing
Discount
malaria
Infectious Diseases

Keywords

  • Costs of variety
  • Drug assortment
  • Drug resistance
  • Healthcare supply chains
  • Treatment heterogeneity

Cite this

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title = "Impact of treatment heterogeneity on drug resistance and supply chain costs",
abstract = "The efficacy of scarce drugs for many infectious diseases is threatened by the emergence and spread of resistance. Multiple studies show that available drugs should be used in a socially optimal way to contain drug resistance. This paper studies the tradeoff between risk of drug resistance and operational costs when using multiple drugs for a specific disease. Using a model for disease transmission and resistance spread, we show that treatment with multiple drugs, on a population level, results in better resistance-related health outcomes, but more interestingly, the marginal benefit decreases as the number of drugs used increases. We compare this benefit with the corresponding change in procurement and safety stock holding costs that result from higher drug variety in the supply chain. Using a large-scale simulation based on malaria transmission dynamics, we show that disease prevalence seems to be a less important factor when deciding the optimal width of drug assortment, compared to the duration of one episode of the disease and the price of the drug(s) used. Our analysis shows that under a wide variety of scenarios for disease prevalence and drug cost, it is optimal to simultaneously deploy multiple drugs in the population. If the drug price is high, large volume purchasing discounts are available, and disease prevalence is high, it may be optimal to use only one drug. Our model lends insights to policy makers into the socially optimal size of drug assortment for a given context. {\circledC} 2013 The Authors.",
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Impact of treatment heterogeneity on drug resistance and supply chain costs. / Spiliotopoulou, Eirini; Boni, Maciej F.; Yadav, Prashant.

In: Socio-Economic Planning Sciences, Vol. 47, No. 3, 09.2013, p. 158-171.

Research output: Contribution to journalArticleScientificpeer-review

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