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Last Updated: 14/02/2023

The cost-effectiveness of molecular epidemiology to inform malaria control program decision-making: a scoping exercise

Objectives

To collect information on the costs and potential impact of using new genetic tools for malaria elimination.

Principal Investigators / Focal Persons

Angela Devine

Rationale and Abstract

Much of the Asia-Pacific is moving toward malaria elimination. In order to get there, we need to find the most efficient way to use the available tools. Molecular epidemiology has the potential to provide a range of additional tools, but it is unclear how this technology will be operationalised. This includes testing for drug resistance at the population and individual level, determining the flow of parasites between geographic areas and checking whether cases in an elimination setting are due to local transmission or importation.

A key question will be under which circumstances is the application of molecular epidemiological approaches in the field an efficient use of resources. In order to do this, we need information on the costs associated with obtaining and analysing genetic data (including training and establishment of a reference laboratory). Importantly, information will also be needed on which actions (such as switching drug regimens, diagnostic testing or targeting interventions to specific geographical regions) could be implemented based on information from genetic data and their associated costs and outcomes. Such actions could lead to resources being targeted to the areas where they can have the greatest impact, save lives, reduce the length of hospital stays and, most importantly, shorten the time to malaria elimination.

Study Design

Surveillance of drug resistance to blood-stage treatments in P. falciparum at the population level will be used as a case study to answer the following questions:

  1. What information is needed on the costs of molecular surveillance (i.e. lab costs, transportation of samples, data analysis and training)?
  2. What are the ways in which this information would be translated into programmatic changes?
  3. What are the costs associated with the potential programmatic changes?
  4. How would the impact of programmatic changes be measured and compared to not switching drugs sooner?
  5. What sort of model would be needed for the CEA?
  6. Can we collect all of the data that we need alongside the currently funded studies?

Question 1 will be answered through interviews with ACREME partners who conduct research in molecular epidemiology (Dr Robinson, Dr Auburn, A/Prof Barry and their teams), providing detailed estimates and projections on the costs. Questions 2-4 will be addressed through a trip to two sites in high and low prevalence settings in PNG where the data for real time spatial support systems is being collected. In addition, a workshop with the technical working group will provide information on what is feasible in country. Activities in PNG will be facilitated by Moses Laman, Leanne Robinson and an Epidemiology and Surveillance Officer that will soon be hired for the DFAT grant. The preliminary data gained from questions 1-4 will be critical for planning a model-based cost-effectiveness analysis (question 5) and apply for its funding (question 6).

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