2. Enhancing the rational use of antimalarials: The cost-effectiveness of rapid immunochromatographic dipsticks in sub-Saharan Africa

3. Malaria Diagnosis Current practice – presumptive treatment (WHO,1999)
ACTs are expensive
Misdiagnosis
Rapid dipstick tests are being developed for simple diagnosis
WHO (1996) IMCI Information Package

5. Study Questions
At what levels of malaria prevalence is dipstick diagnosis cost-effective?
How much should we be willing to pay for further information about model parameters before making a decision?

6. Simple decision tree model
Sensitivity Specificity
PT: 100% % 0% 100%
Dip: 86-94% % % 1-28%

7. Probabilistic sensitivity analysis
Uncertainty around most parameters represented by lognormal and beta distributions
Incremental cost-effectiveness ratios (ICERs) calculated probabilistically using Monte-Carlo simulation
ICERs converted to net-benefit
Net Benefit = Effects * λ – Costs
The ceiling ratio (λ) is US $150 per DALY averted (WHO, 1996)
WHO (1996) Report of the Ad Hoc Committee on Health Research Relating to Future Investment Options

8. Probability Cost-Effective Ceiling Ratio = $150/DALY averted
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9. Incremental Net-benefit
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10. Calculation of EVPI Ceiling Ratio = $150/DALY averted
Iterations
Net Benefit Dipsticks
Net Benefit PT
Net Benefit WPI 1
$40
$20 2
$25 3
$35 4
$30
Average
$32.50
EVPI =
$ $30 = $2.50

11. EVPI according to prevalence
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12. Discussion Cost-effectiveness most sensitive to Further benefits
Epidemiological setting
Cost and accuracy of dipsticks
Probability patients return for treatment
Further benefits
Reduce drug pressure and development of drug resistance
Encourage use of treatment facilities
Epidemiological surveillance

13. Limitations of the model
Assumes that health workers and patients trust and follow dipstick results
False positive diagnoses are not well defined
Does not consider private sector
Not applicable in areas where microscopy is currently in use
EVPI depends on number of variables you include in your model

14. Further work
Conduct a EVSI analysis to determine which parameters warrant further testing
Consider parasite density, immunity, and health worker behaviour
Determine affordability of dipsticks at a national level
Predict impact on drug resistance

15. Acknowledgements Chris Whitty Sarah Staedke Shunmay Yeung
Andrew Briggs
Funders: UK Department for International Development, LSHTM Health Economics and Financing Programme