Transcript Poster presentation

Enhancing the rational use of
antimalarials: The costeffectiveness of rapid
immunochromatographic
dipsticks in sub-Saharan Africa
Chantal Morel, Sam Shillcutt, Paul Coleman, Catherine Goodman
& Anne Mills
Health Economics and Financing Programme, Public Health & Policy Department
Disease Control & Vector Biology Unit, Infectious and Tropical Diseases Department
The London School of Hygiene & Tropical Medicine
Abstract
Problem Statement: The massive burden of malaria, along with a severe scarcity of economic resources, makes
efficiency in antimalarial drug programs a critical issue in sub-Saharan Africa. Parasite resistance has
developed to currently-used first line therapies, to which artemisinin-based combination therapies (ACTs)
provide a cost-effective alternative. Rapid immunochromatographic dipsticks may be an efficient method in
certain settings to allocate these more-expensive but more-effective drugs.
Objectives: This study evaluates the cost-effectiveness of using dipsticks to diagnose malaria in sub-Saharan
Africa relative to presumptive antimalarial treatment for all people presenting to a clinic with fever. To set
an upper limit for how much a decision maker should be willing to pay to reduce parameter-uncertainty
within the model, the expected value of perfect information (EVPI) is calculated.
Design: A theoretical decision-analytic model is used to determine the probability that dipsticks are costeffective across a spectrum of possible prevalence levels. Drug savings are measured according to
unnecessary treatments avoided through improved accuracy of diagnosis. Given the uncertainty
surrounding cost-effectiveness estimates, the per-person EVPI is calculated.
Setting: Sub-Saharan Africa.
Population: A hypothetical population presenting with fever to a clinic for treatment.
Intervention: Immunochromatographic dipsticks may be used to diagnose malaria in approximately 20 minutes.
After blood and buffer are mixed in a sample well, immersion of an antibody-covered strip will indicate the
presence of malaria parasites.
Outcome Measures: Incremental Cost per Disability Adjusted Life Years (DALYs).
Results: At a ceiling ratio of US$150/DALY averted, it is 95% certain that dipsticks are cost-effective where
fewer than 15% of febrile patients have parasitemia, and not cost-effective above 55%. The EVPI is greatest
between 15% to 55% prevalence with a peak at 33%, the point at which uncertainty around the costeffectiveness of dipsticks is at its maximum.
Conclusions: Based on criteria of economic efficiency, dipsticks should be used in areas where the proportion of
febrile illnesses caused by malaria is low. The simplicity and clarity of this diagnostic strategy is likely to
provide incentives to encourage people to seek treatment, encourage more rational use of ACTs, and impede
the development of resistance to ACTs.
Study Questions

At what levels of malaria prevalence is
dipstick diagnosis cost-effective relative to
presumptive treatment?

How much should a decision-maker be willing
to pay to eliminate uncertainty about model
parameters before making a decision?
Introduction
Inappropriate diagnosis of febrile illness is a common problem in
sub-Saharan Africa. Presumptive treatment for malaria dominates
where malaria is prevalent, which leads to excessive prescription of
antimalarials and inappropriate treatment of non-malarial fevers. These
fevers may become severe with delayed treatment. With the
introduction of artemisinin-based combination therapies, presumptive
treatment may no longer be affordable. Rapid dipstick tests are an
inexpensive and simple diagnostic tool, and are currently being
developed for use in endemic areas. This paper examines the costeffectiveness of using dipsticks to diagnose malaria, given treatment
with ACTs, across the possible range of malaria prevalence in lowincome countries of sub-Saharan Africa.
Beyond the scope of the cost-effectiveness analysis, it is important
to consider the value of collecting additional information about
parameter values. Both deciding to implement an intervention and
deciding to obtain further information involve potential opportunity costs
– choosing a sub-optimal intervention, or spending money to confirm an
existing recommendation. An EVPI analysis may be used to evaluate
the maximum value that further information could add to the model.
While EVPI does not determine the value of information given by
studies with finite sample sizes, it provides a threshold above which the
option to sample further can be rejected. This study estimates the EVPI
for the overall model.
Methods
A simple decision tree, restricted to patients that present with
fever to a public health facility, was developed to calculate
incremental cost-effectiveness. The decision tree in Figure 1 follows
an individual patient entering the system through to being cured,
dying, or surviving with neurological sequelae, according to the
sensitivity and specificity of each diagnostic strategy and level of
malaria prevalence. Evidence on the progression of non-malarial
illnesses is lacking, and it was assumed that their consequences
would be similar to untreated malaria.
All parameter values, their associated uncertainty, were
abstracted from a variety of sources and sub-Saharan African (SSA)
countries. A population structure including 50% adults and 50%
children was assumed in the model.
Costs, in 2002 US dollars, were calculated using the
ingredients approach. Only direct costs of medical diagnosis and
care were included in this analysis. A range of ACTs were
considered, including artesunate-sulfadoxine-pyrimethemine,
artemether-lumefantrine (Coartem™), and artesunate-mefloquine1.
Drugs recommended by the Integrated Management of Childhood
Illness (IMCI) protocol for febrile illness were considered for negative
diagnoses: paracetamol, amoxicillin, and chloramphenicol2.
1. Bloland (2001) WHO 2. WHO (1999) IMCI Information Package
Methods
Health outcomes were measured in terms of DALYs
averted, calculated according to standard methods. Full
compliance with diagnosis and treatment was assumed on the
part of the patient and the health worker.
Parameter uncertainty was quantified using probabilistic
sensitivity analysis, and incremental cost-effectiveness ratios
(ICERs) were determined. The probability dipsticks are costeffective was evaluated using a ceiling ratio equal to
US$150/DALY averted (λ)1. ICERs were converted to netbenefits using the following formula.
Net Benefit = Effects * λ – Costs
Expected Value of Perfect Information (EVPI) was
calculated according to methods shown in Figure 22. The
average net-benefit of the optimal strategy at each iteration was
used to approximate the expected value of making a decision
under complete certainty. The difference between this and
expected net benefit with current uncertainty is the EVPI.
1. WHO (1996) Investing in Health Research and Development, Report of the Ad Hoc
Committee on Health Research 2. Fenwick (2000) York Discussion Paper
Figure 1: Simple decision tree model
sensitivity, a
malaria, p
Suspected
malaria
True positive
1-a
False negative
specificity, b
True negative
1-b
False positive
1-p
• Give all suspected malaria ACTs: a=1 and b =0
• Use dipstick before giving ACTs: a0.95 and b 0.95
Figure 2: Calculation of EVPI
Ceiling Ratio = $150/DALY averted
Iterations
Net Benefit
Dipsticks
Net Benefit PT
Net Benefit WPI
1
$40
$20
$40
2
$20
$25
$25
3
$35
$25
$35
4
$25
$30
$30
Average
$30
$25
$32.50
EVPI =
$32.50 - $30 = $2.50
$40
$20
-$40
-$60
-$80
Malaria Prevalence
10
0%
90
%
80
%
70
%
60
%
50
%
40
%
30
%
20
%
-$20
10
%
$0
0%
Incremental Net Benefit
Figure 3: Incremental Net-benefit
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
$2.50
$2.00
$1.50
$1.00
$0.50
0%
$0.00
20% 40% 60% 80% 100%
Malaria Prevalence
EVPI per person
Probability costeffective
Figure 4: Probability Cost-Effective and EVPI
Probability costeffective
EVPI per per
person
Discussion of Results
Rapid dipstick tests will introduce a tradeoff between
reducing the prescription of antimalarials with reducing the
sensitivity of diagnosis. Our model indicates that where 15% or
fewer fevers are caused by malaria, dipsticks are the dominant
strategy, and should be used in public health care clinics to
diagnose malaria. This result is most sensitive to malaria
prevalence and the cost and accuracy of dipsticks. When
prevalence is high, the probability that a person will return for
treatment if symptoms become severe is important.
Reducing amounts of antimalarials prescribed may affect
drug pressure on parasites, which would impact the growth of
drug resistance. However, improved diagnosis may increase
compliance to ACTs (around 40%)1, and use of the public health
care system among people receiving antimalarials (around
50%)2. Thus, the net effect on drug pressure is unclear.
Improved information on prevalence may help health
planners more effectively target preventive and treatment
measures towards people who need them most, both within the
context of malaria and across disease areas.
1. Depoortere (2004) TMIH
2. Foster (1991) WHO Bull
Limitations and Further Work
This analysis is limited in several respects. It assumes that
patients and health workers will follow the mode of action suggested by
the dipstick results, restricting drug treatment to those with positive
tests. In reality, patients who test negative may be given antimalarials.
Health workers may lack faith in test quality, and patients may demand
drugs anyway. In areas of high transmission intensity, some patients
may be immune to levels of malaria parasites that cause illness in
others. The interaction of these factors pose complex questions for
diagnostics that are not dealt with in this model.
Further work is necessary to clarify the causes of treatable febrile
illness in people who incorrectly receive antimalarials. Some evidence
exists to suggest that pneumonia, salmonella, meningitis, and other
illnesses are common. Treatments and outcomes for these diseases
differ, and studies are needed to determine their relative contributions
to misdiagnosis.
Our EVPI estimate provides only a rough estimate of the
maximum amount a decision-maker should be willing to pay for perfect
information in the entire model. A more useful analysis would estimate
the value of testing individual parameters according to the power
associated with specific sample sizes. A Bayesian two-step MonteCarlo simulation approach has recently been developed to make this
analysis possible1.
1. Brennan (2004) J. Health Economics
Conclusion and Policy Implications
Rapid dipstick tests are highly effective and simple tools for
diagnosing malaria. Our model suggests that they should be used
where 15% or fewer people that present to public health clinics with
fever have malaria. These results should not be interpreted
according to endemicity as transmission intensity and parasitemia
are not linearly correlated. Further information to reduce uncertainty
around model parameters may be useful between 15% and 55%
prevalence. However, if these studies are projected to cost more
than US$2.20 per person, decision-makers should proceed with the
choice to adopt dipsticks.
Dipsticks represent a significant investment, costing between
US$0.50 and US$1.85 per test1, or about one-half as much as firstline treatment with ACTs2. Currently, 42% of malaria costs are borne
by households in SSA, with 39% covered by donors3. The
international community must contribute to this efficient use of
resources to combat this disease.
1. Kindermans (2002)
2. Bloland (2001) 3. WHO (2003) Africa Malaria Report