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Using top-down and bottom-up costing approaches in LMICs: The case for using both to assess the incremental costs of new technologies at scale

Research output: Contribution to journalJournal articlepeer-review

Published
  • Lucy Cunnama
  • Edina Sinanovic
  • Lebogang Ramma
  • Nicola Foster
  • Leigh Berrie
  • Wendy Stevens
  • Sebaka Molapo
  • Puleng Marokane
  • Kerrigan McCarthy
  • Gavin Churchyard
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<mark>Journal publication date</mark>1/02/2016
<mark>Journal</mark>Health Economics
Issue numberSupp 1
Volume25
Number of pages14
Pages (from-to)53-66
Publication StatusPublished
<mark>Original language</mark>English

Abstract

Purpose
Estimating the incremental costs of scaling‐up novel technologies in low‐income and middle‐income countries is a methodologically challenging and substantial empirical undertaking, in the absence of routine cost data collection. We demonstrate a best practice pragmatic approach to estimate the incremental costs of new technologies in low‐income and middle‐income countries, using the example of costing the scale‐up of Xpert Mycobacterium tuberculosis (MTB)/resistance to riframpicin (RIF) in South Africa.

Materials and methods
We estimate costs, by applying two distinct approaches of bottom‐up and top‐down costing, together with an assessment of processes and capacity.

Results
The unit costs measured using the different methods of bottom‐up and top‐down costing, respectively, are $US16.9 and $US33.5 for Xpert MTB/RIF, and $US6.3 and $US8.5 for microscopy. The incremental cost of Xpert MTB/RIF is estimated to be between $US14.7 and $US17.7. While the average cost of Xpert MTB/RIF was higher than previous studies using standard methods, the incremental cost of Xpert MTB/RIF was found to be lower.

Conclusion
Costs estimates are highly dependent on the method used, so an approach, which clearly identifies resource‐use data collected from a bottom‐up or top‐down perspective, together with capacity measurement, is recommended as a pragmatic approach to capture true incremental cost where routine cost data are scarce.