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Anna Devereaux | April 2026 Peer Review https://doi.org/10.64898/2026.03.31.26349813

Summary

Defining the Potential Impact and Cost Effectiveness of a non-invasive Diagnostic for Malaria: A Modeling Study assesses the relative cost effectiveness and population impact of Non-Invasive Diagnostic (NIDs) tools compared to Rapid Diagnostic Tests (RDTs) and traditional diagnostics for Malaria. Researchers propose that NIDs may be a more efficient and cost effective alternative to detect malaria in overburdened populations. NIDs do not require a blood sample, relying on DNA from sampled saliva or hair cells, and have a turnaround time of 5 minutes. Using a decision-tree analysis to test four simulated diagnostic scenarios in a hypothetical cohort of 10 million, Hansen finds that the NID’s strong sensitivity feature may enable more accurate case detection at lower productivity costs while also increasing the risk of antimicrobial overprescription. Opposed to RDT diagnostics, the test’s high sensitivity feature consequently increased false positives, potentially contributing to the development of population- level drug resistance. Considering the lower productivity costs of NIDs, their ability to test symptomatic and asymptomatic at increased rates, and the projected outcome of overprescription, the study concludes scenario 4 to be the most efficient Malaria detection and diagnosis approach. A threshold sensitivity and specificity for NID tests at a minimum 55% asymptomatic sensitivity and at least 84% specificity can improve case detection, reduce antimicrobial overuse, limit costs, and may be most effective when followed by an RDT.

The study addresses a prominent global concern, that despite widespread availability, full population coverage with RDT-based testing has not been achieved due to constraints that limit universal utilization in malaria-endemic regions. While the comprehensive modeling approach of 10 million individuals strengthens the simulation’s assessment of various epidemiological settings and Malaria prevalence, this aggregate model may fail to capture constraints observed at the local level. Therefore, simulated cost savings and diagnostic gaps may overestimate the real-world impact of NIDs if local manufacturing and health systems do not have the capacity to roll-out this intervention, and if the model biases are not thoughtfully adjusted.

Major Concerns

The cost effectiveness analysis is limited and includes biases that result in overestimations of NID cost savings that ignore the potential for cost additive scenarios. The study assumes perfect use of the NID device across the hypothetical cohort of 10 million people and fails to capture the impacts of underutilization in real-world settings.

The cost effectiveness of a diagnostic test requires access to and uptake of the device to compensate for its production. Given contextual barriers that may prevent communities from accessing clinics with NID devices, it is unrealistic to assume total utilization. There may be biased projections of DALYs since the study does not take into account behavioral limitations (seeking care or not) and assumes full geographic implementation and scale-up without accounting for transmission dynamics. The simulations also do not account for future training and maintenance costs that will incur to sustain upkeep of these devices, omitting significant costs from the analysis.

In regard to the diagnostic procedure, the simulations do not measure likely detection lags, given that the protein biomarkers for NIDs reach detectable levels at slower rates in the saliva samples than they do in blood samples. The impact of reducing asymptomatic infections cannot be fully assessed without the use of a transmission model. Because significant costs are not included, it is not possible to comprehensively determine the comparable effectiveness of NIDs compared to RDT-based scenarios, posing an ethical dilemma. RDTs are proven to work (despite the criticisms presented in the paper), so diverting resources away from this intervention to support an intervention based on modeled assumptions that exclude costs and real-world caveats may not be a sustainable approach.

Minor Concerns 

Hypothetical cohorts cannot capture healthcare access, workforce dynamics, and infrastructure in local contexts. The scalability of non-invasive technology depends on manufacturing capacity, supply chain logistics, and integration into existing health systems. The study depends on the efficiency of the test to produce rapid results, but the positive impacts of quicker turnaround times cannot be met if structural capacity does not exist. Real-world diagnostic accuracy is not established, so true cost-effectiveness and population health gain would depend on whether or not base- case assumptions (used in the model) are met.

Actionable Recommendations 

Researchers can consider incorporating a transmission model into their decision tree analysis to accurately project DALYs that account for prevention dynamics, not solely asymptomatic case identification. The model simulations should also include a sensitivity analysis on NID devices, capping it between 60-80% to reflect real-world access and uptake in low resource and rural regions. An amortization calculation should also be incorporated to model maintenance costs as a percentage of initial capital expenditure.

Competing Interests Statement

Authors declare no competing interests.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they used generative AI to come up with new ideas for their review.