Diagnostic and Prognostic Research (Aug 2018)

Cost implications for the NHS of using the Alere™ i Influenza A & B near patient test with nasal swabs

  • A. J. Allen,
  • R. A. O’Leary,
  • S. Davis,
  • S. Graziadio,
  • W. S. Jones,
  • A. J. Simpson,
  • D. A. Price,
  • L. Vale,
  • M. Power

DOI
https://doi.org/10.1186/s41512-018-0031-8
Journal volume & issue
Vol. 2, no. 1
pp. 1 – 8

Abstract

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Abstract Background Influenza is an acute viral infection of the respiratory tract. A rapid confirmatory diagnosis of influenza is important, since it is highly transmissible and outbreaks of influenza within the hospital setting increase morbidity and mortality. The objective of this study was to evaluate the cost implications, from the perspective of the UK NHS, of using on-label nasal swabs with the Alere™ i Influenza A & B test in a near patient setting. Methods A cost consequence model was developed. The time horizon of the model was from hospital admission on suspicion of influenza until the end of treatment (following a diagnosis of influenza or discharge from hospital). Data on the prevalence of influenza and the sensitivity and specificity of the Alere™ i Influenza A & B test came from two prospective observational diagnostic accuracy studies. Costs were obtained from published resources. Uncertainties in the model data were investigated using deterministic, one-way sensitivity analyses. Results Using the Alere™ i Influenza A & B point of care test with nasal swabs (on label) in NHS medical assessment units and emergency departments could save approximately £242,730 per 1000 adults presenting with influenza-like symptoms. The main cause for this was reduced times to availability of the result compared with the laboratory RT-PCR test. Other key drivers of savings were the cost of isolation, the prevalence of influenza, the specificity of the test, and the availability of isolation resources. Conclusions The Alere™ i Influenza A & B point of care test would have greatest impact in hospitals that have extensive delays in the time to receive a result. Sensitivity analyses identified the model parameters which would have greatest effect on the result and confirmed that assumptions were conservative, i.e. did not change key results.

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