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ABSTRACT |
 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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However, the costs of the new ACTs are likely to be an order of magnitude more expensive than current therapies.7,8 Therefore, it is important to examine formally the cost-effectiveness of the potentially more effective yet more expensive ACTs before advocating a switch in policy. Importantly, any such cost-effectiveness analysis must consider the temporal dynamics of drug resistance and not just focus on the static question of whether switching today would be cost-effective at current levels of resistance; as drug resistance emerges and spreads, the cost-effectiveness ratio changes depending on what time frame of analysis is chosen.9 These temporal considerations are particularly important in the case of ACTs, since the potential consequences are grave of having widespread resistance to artemisinin and its derivatives at some point in the future, without any alternative therapies.
However, predicting the future trajectory of drug resistance is extremely difficult, particularly for ACTs, which have yet to be widely used as first-line therapy in sub-Saharan Africa. As a result, basic knowledge about drug usage patterns, and the time to emergence and the rate of spread of resistance, are not yet available. In the absence of such basic parameters, we propose a theoretical approach to help understand the threshold conditions under which the introduction of ACTs is likely to be cost-effective relative to the strategy of retaining a monotherapy as first-line treatment. The analysis uses a simple decision tree framework that allows the disease burden and costs associated with a first-line antimalarial treatment to be estimated, while explicitly taking into account the growth of resistance to treatments through time. The threshold conditions under which ACTs are cost-effective relative to retaining a monotherapy are described as a function of two main variables: 1) the growth rate of resistance of the ACT therapy relative to the monotherapy, and 2) the starting level of resistance of the monotherapy. While the model is generic, in that it can be used to examine a range of alternative first-line scenarios, we restrict our detailed analysis to comparing the cost-effectiveness of artemisinin-based combination drugs relative to a baseline scenario of retaining SP over a set period of time. The importance of the results and the usefulness of the novel quantitative approach are discussed.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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, follows the possible outcome of a patient presenting with malaria and receiving first-line treatment. In the first instance, the patient may be either cured or experience treatment failure. Cure was defined as adequate clinical response following criteria of the World Health Organization.10 The average health consequences (expressed as disability adjusted life years [DALYs]) and costs (US$ 2002) per patient associated with a given first-line drug regimen, may then be calculated by following the decision tree paths to the end points as shown in Figure 1 and Table 1. The decision is considered from a provider’s perspective, and is restricted to those true malaria cases presenting at formal outpatient facilities, with the out-patient population weighted by the proportions <5 years old and 
5 years old (Table 1).11
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and Table 1) with a given first line drug, i, at time t was defined as being dependent on three main inputs: first, the proportion of malaria parasites, R, resistant to drug i at time t; second, the probability, m, that a patient complies at the recommended dose with drug therapy i; and third, the probability, p, that therapy i is effective despite the patient failing to comply fully with the treatment regimen. We assumed that any patient infected with resistant parasites experiences treatment failure, regardless of whether they fully comply with the treatment regimen. As such, there are only two possible ways in which treatment with drug i at time t may be successful. First, the patient is not infected with resistant parasites and fully complies with the treatment regimen; the probability of this occurring is (1 – Ri,t)mi. Alternatively, the patient may be infected with susceptible parasites but, despite not complying with the treatment regimen, is still cured; the probability of this occurring is (1 – Ri,t)(1 – mi)pi. The overall probability of treatment success is the sum of these two probabilities, while treatment failure is simply one minus treatment success. Thus
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defines treatment failure, which simplifies to
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The dynamic spread of drug resistance through time was modeled using a logistic growth function of the form
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where Ri,0 is the level of drug resistance to treatment i at the start of the N-year time period; ki is the maximum possible level of drug resistance, which cannot exceed 1 (i.e., at ki = 1 the entire malarial parasite population would be resistant to drug i); and ri is the maximum growth rate of resistance against drug i, which occurs when Ri,0 approaches zero.
We assume mi and pi remain constant with time over the N-year period of evaluation, although in practice patient compliance is likely to be influenced by the effectiveness of the drug (and in turn, the rate of development of resistance will also be dependent on patient usage patterns). The dynamics of treatment failure, as described by equation 2, are driven by the growth of resistance given by equation 3. To illustrate this, Figure 2 shows the temporal changes in treatment failure for two alternative drug regimens used over a 10-year period as predicted by equations 2 and 3.
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and Table 1). Similarly, the average DALY burden associated with treatment failure, ei,F, and success, ei,S, may also be estimated. The total costs, C, over the full N-year period associated with using drug i as the first line treatment, were then calculated as
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and the total DALY burden, E, incurred over the same period was estimated as
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where 
is the annual discount rate. The incremental cost effectiveness ratio (ICER) of using an ACT relative to the existing drug, X, over the full N-year period was given as
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The DALYs were calculated using standard methods,12 assuming age-specific life expectancies based on a United Nations west African life table with average life expectancy of 50 years at birth and a 3% discount rate. The costing methodology followed that described by Goodman and others,11,13 and used the ingredients approach, with all prices converted to 2002 US dollars.
Drug regimen comparison and parameter values. The model was anchored to the real world by using the framework to compare the use of either SP or an ACT as a first-line therapy in a high transmission setting of a low-income, sub-Saharan African country (as defined by Goodman and others11). A baseline time period of N = 10 years (varied to a minimum of 5 years and maximum of 15 years) and a discount rate of 3% (varied to 0%) per year were chosen.
The maximum growth rate of SP resistance, rSP, was estimated from longitudinal drug resistance studies conducted in eastern and southern Africa.14 The maximum growth rate of ACT, rACT, was defined relative to rSP, with the ratio rACT/rSP varied from 0.05 upwards. The baseline starting condition for ACT resistance at t = 0 was set at 0.001 (i.e., one parasite per 1,000 would show resistance to the ACT), and was also varied to a maximum of 0.01 (i.e., one parasite per 100 showing resistance).
All input parameter values and their sources are shown in Table 1. Scarcity of data restricted parameter definitions to best estimates and likely ranges, represented by triangular distributions.9 However, to redistribute probability density to its most likely form, standard distributions were fitted to the triangles by minimizing the square of the distance between the two distributions. Standard beta distributions, bounded by 0 and 1, were appropriate for parameters representing probabilities. Lognormal distributions, bounded by 0 with a positive skew, were appropriate for parameters representing costs, since there is a marginal likelihood that variable costs may be disproportionately high. Point estimates were used for parameters for which uncertainty was not reported or unknown, such as disability weights, or parameters that were known with absolute certainty.
A continuous, linear cost function with zero fixed costs was assumed. Consequently, a child’s dose, assumed to be 50% of an adult dose, cost 50% of the adult dose. The range for the cost of ACT was made from estimates for artesunate/SP (low estimate), Co-Artem® (Novartis International AG, Basel, Switzerland) artemether-lumefantrine (best estimate), and artesunate/mefloquine (high estimate).
The model does not take into account policy change costs. In practice, a range of costs, such as improved diagnostics and regulatory measures, may accompany a change in treatment regimes (e.g., to a more advanced drug such as ACT). The magnitude of these costs is currently unknown.
Monte Carlo simulation and threshold estimation.
For combinations of SP starting resistance and values of rACT/rSP, the model was iterated 3,000 times. At each iteration, input parameter values were chosen at random from the probability distributions defined in Table 1, and the ICER of using an ACT relative to SP over the N-year time period calculated according to equation 6. The calculated ICER was compared with a cut-off value of $150/DALY averted, a rough economic evaluation criterion by which a health intervention in a developing country may be judged to be "attractive".15 For any starting condition of SP resistance, the threshold value of the ratio rACT/rSP at which 95% of the model iterations were cost-effective at the $150/DALY level was then recorded. A cost-effectiveness probability surface was drawn to show the threshold conditions under which the use of the ACT over the N-year period would be considered cost-effective with 95% certainty at the $150/DALY level.
Standard acceptability curves were constructed to represent uncertainty around the $150/DALY averted decision rule, and to make cost-effectiveness results comparable to opportunity costs represented by other medical interventions. To make this analysis possible, we chose scenarios with starting levels of resistance to SP at 10%, 30%, and 50%, and with resistance growing at an equal trajectory for both drugs (rACT/rSP = 1). To quantify the levels of cost-effectiveness given in these figures, and to allow for considerations of affordability, per-person incremental costs, incremental DALYs averted, and cost-effectiveness ratios were recorded.
The model iterations were conducted in Microsoft (Redmond, WA) Excel® using the @Risk add-in tool (Palisade Corporation, Newfield, NY).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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. For the baseline scenario with a 10-year period of comparison, 3% annual discount rate, and starting ACT resistance frequency of 0.001, the threshold values of the ratio rACT/rSP vary from approximately 1.5 (at a 10% starting level of SP resistance) to 5 (at a 60% starting level of SP resistance) i.e., resistance to ACT would have to grow 1.5 times faster than resistance to SP monotherapy for acts to be the preferred policy option. Above 60% resistance to SP, ACTs are predicted to be cost-effective at all considered values of rACT/rSP.
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Figure 4 shows the impact of increasing the starting condition of ACT drug resistance by an order of magnitude from 0.001 to 0.01. For all times frames, the critical threshold value of the rACT/rSP ratio was significantly lower when using the higher starting frequency of ACT resistance. The effects of varying the discount rate to zero were negligible and the results did not noticeably differ from those shown in Figures 3 and 4.
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. As starting levels of resistance to SP increase, cost-effectiveness becomes more robust to changes in the decision rule over a five-year time horizon. The decision rule has no impact on cost-effectiveness after 10 years or 15 years.
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and 3 show the means of incremental per-person costs incurred, incremental DALYs averted, and ICER for each starting condition for SP-resistance and an rACT/rSP ratio equal to 1. As RSP,0 (i.e., the starting level of resistance to SP at time t = 0) increases, incremental costs decrease, incremental DALYs increase, and ACTs become more cost-effective.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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The results of the simulations highlight the importance of the time frame of economic evaluation when evaluating dynamic systems.9 Taking a relatively short-term perspective (five years) shows a threshold of SP resistance below which it is not cost-effective to switch to an ACT, using the 95% decision rule. No such lower SP resistance threshold is seen with the longer 10-year and 15-year time frames. Across much of eastern and southern Africa, the level of SP drug resistance is already approximately 30% or more.14 At such levels of SP resistance, the model is consistent in its predictions, regardless of the time frame, or the assumption of initial ACT resistance levels, and the growth rate of resistance of the ACT would have to be significantly greater than that observed with SP for the ACT not to remain cost effective. Moreover, as is intuitively predictable, the higher the level of SP resistance, the more rapidly ACT drug resistance would have to spread for it not to be a cost-effective replacement strategy. At high initial levels of SP resistance, the decision to switch to ACTs is nearly certain, resistance to ACTs would have to grow extremely rapidly for them not to be cost-effective.
It should also be noted that the predicted thresholds are conservative for two reasons. First, the chosen comparator, SP, has a higher estimated compliance (since it is taken as a single dose), and thus a lower expected failure rate for a given level of resistance, relative to other existing therapies such as amodiaquine and chloroquine. Second, the threshold condition under which the ACT strategy fails to be cost-effective was based on a stringent 95% decision rule. It has been argued that a 50% threshold should be adopted as a basis for determining cost-effectiveness.20 Adoption of a lower certainty criterion would raise the level of the threshold boundary across all time frames (i.e., expand the cost-effective area of the economic evaluation planes shown in Figure 3). Nevertheless the 95% cut-off is useful since decision makers tend to be risk adverse in sanctioning policy changes,9 and given the financial implications of advocating a significantly more expensive drug, the greater the certainty of cost-effectiveness the more helpful is the analysis in informing policy decisions.
To interpret the results from this model, consideration must be made of the unregulated prescription of combination therapy drugs. If one or both of the component drugs is made available individually through the private or informal sectors, resistance to the combination therapy is likely to develop more rapidly. A similar argument may be made for drugs that act through similar mechanisms, such as artesunate and artemether, in which resistance to one drug will have negative effects on the efficacy of the other. However, the availability of drugs through the private sector will impact on the growth of resistance of all alternative first-line therapies, ACTs as well as currently used therapies such as SP. Therefore, private sector availability may be neutral in its impact on the relative growth of ACT and SP resistance. However, even if private sector availability impacts differentially more on the growth rate of resistance to ACT, it seems unlikely that the impact could be so great as to alter the decision for a policy switch under the majority of the settings represented by the parameter space in Figures 3 and 4.
The structure of the model is flexible in that it can be adapted to examine a range of alternative first-line treatments, and, through varying the input parameters, to analyze specific epidemiologic and economic scenarios. The framework may also be used to quantify the influence of key variables on the location of the threshold conditions. Three key variables that should be further examined as more empirical data become available are 1) the expected cost of the ACT, including not only drug price but other costs associated with its implementation, 2) the level of compliance to the ACTs, and 3) the initial level of drug resistance, as shown by the difference in threshold conditions in Figure 3 (initial ACT resistance set at 1 in 1,000) and Figure 4 (initial ACT resistance set at 1 in 100).
While the model performs well in avoiding the problem associated with a lack of basic knowledge on the key parameter of ACT growth of resistance, it has several limitations that may influence the predicted outcomes. First, the framework is based on a previous model that focused only on the population seeking care through formal inpatient/outpatient facilities. It does not consider the whole population at risk of malaria, nor accommodate the range of patient-seeking behaviors in, for example, the private sector. Second, although a logistic function is used to capture the temporal dynamics of resistance, there is no link between the growth rate of resistance and those factors dictating the evolution of resistance, such as drug pressure, compliance to drug regimens, drug decay rates, and parasite recombination rates. Also, the links between levels of treatment failure, patient compliance, and overall patterns of drug usage are also dynamic, which is not captured. Third, the effects of transmission intensity and other malaria interventions are not included; rather the model is restricted to the presentation of true malaria-positive individuals and does not accommodate changes in underlying rates of malaria in different sections of the population. Fourth, cost-effectiveness is only one criterion on which policy decisions are based. Importantly, cost-effectiveness estimates are not informative for issues of affordability. Since the exclusive use of a cost-effectiveness threshold theoretically can lead to a prescription for unlimited expansion of the health service, it will be necessary to account for affordability in further work.
While all four shortfalls of the approach should be addressed by future research, ideally through theoretical developments guided by empirically measured parameters, the model presented here provides a structured means of robustly quantifying the likely range of conditions under which switching from SP to an ACT is likely to be cost-effective. Crucially, given current limited production of ACTs, a considerable amount of forward planning and logistical support must be in place to ensure that ACTs are widely available before advocating a switch in policy.
Received August 21, 2003. Accepted for publication December 8, 2003.
Acknowledgments: We thank Andrew Briggs for facilitating the collaboration between the Health Economics Research Centre (University of Oxford) and the London School of Hygiene and Tropical Medicine and for his input to the modeling.
Financial support: This study was supported by the Institute of Medicine and the World Health Organization.
Authors’ addresses: Paul G. Coleman, Disease Control and Vector Biology Unit, Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom, Telephone: 44-207-927-2333, Fax: 44-207-580-9075. E-mail: Paul.Coleman{at}lshtm.ac.uk. Chantal Morel, Catherine Goodman, and Anne J. Mills, Health Economics and Financing Program, Department of Public Health and Policy, Health Policy Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom, E-mails: Chantal.Morel{at}lshtm.ac.uk, Catherine.Goodman{at}lshtm.ac.uk, and Anne.Mills{at}lshtm.ac.uk. Samuel Shillcutt, Health Economics Research Centre, University of Oxford, Old Road, Headington, Oxford OX3 7LF, United Kingdom and Department of Economics, School of Social Sciences, City University, Northampton Square, London EC1V 0HB, United Kingdom, E-mail: Samuel.Shillcutt{at}public-health.oxford.ac.uk.
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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